Search results for: Markov chain Monte Carlo method

Authors: Georgios Charizanos, Haydar Demirhan, Duygu Icen

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Two of the most impactful issues in classical logistic regression are class imbalance and complete separation. These can result in model predictions heavily leaning towards the imbalanced class on the binary response variable or over-fitting issues. Fuzzy methodology offers key solutions for handling these problems. However, most studies propose the transformation of the binary responses into a continuous format limited within [0,1]. This is called the possibilistic approach within fuzzy logistic regression. Following this approach is more aligned with straightforward regression since a logit-link function is not utilized, and fuzzy probabilities are not generated. In contrast, we propose a method of fuzzifying binary response variables that allows for the use of the logit-link function; hence, a probabilistic fuzzy logistic regression model with the Monte Carlo method. The fuzzy probabilities are then classified by selecting a fuzzy threshold. Different combinations of fuzzy and crisp input, output, and coefficients are explored, aiming to understand which of these perform better under different conditions of imbalance and separation. We conduct numerical experiments using both synthetic and real datasets to demonstrate the performance of the fuzzy logistic regression framework against seven crisp machine learning methods. The proposed framework shows better performance irrespective of the degree of imbalance and presence of separation in the data, while the considered machine learning methods are significantly impacted.

Keywords: fuzzy logistic regression, fuzzy, logistic, machine learning

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Authors: Adem Acır

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In this study, time dependent neutronic analysis of incineration of minor actinides of a Laser Fusion Inertial Confinement Fusion Fission Energy (LIFE) engine was performed. The calculations were carried out by using MCNP codes with ENDF/B.VI neutron data library. In the neutronic calculations, TRISO particles fueled with minor actinides with natural lithium coolant were performed. The natural lithium cooled LIFE engine used 10 % TRISO fuel minor actinides composition. Tritium breeding ratios (TBR) and energy multiplication factor (M) burnup values were computed as 1.46 and 3.75, respectively. The reactor operation time was calculated as ~ 21 years. The burnup values were obtained as ~1060 GWD/MT, respectively. As a result, the very higher burnup were achieved of LIFE engine.

Keywords: Monte Carlo, minor actinides, nuclear waste, LIFE engine

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Authors: Ebert Brea

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We propose a novel direct search algorithm for identifying at least a local minimum of mixed integer nonlinear unconstrained optimization problems. The Mixed Integer Randomized Pattern Search Algorithm (MIRPSA), so-called by the author, is based on a randomized pattern search, which is modified by the MIRPSA for finding at least a local minimum of our problem. The MIRPSA has two main operations over the randomized pattern search: moving operation and shrinking operation. Each operation is carried out by the algorithm when a set of conditions is held. The convergence properties of the MIRPSA is analyzed using a Markov chain approach, which is represented by an infinite countable set of state space λ, where each state d(q) is defined by a measure of the qth randomized pattern search Hq, for all q in N. According to the algorithm, when a moving operation is carried out on the qth randomized pattern search Hq, the MIRPSA holds its state. Meanwhile, if the MIRPSA carries out a shrinking operation over the qth randomized pattern search Hq, the algorithm will visit the next state, this is, a shrinking operation at the qth state causes a changing of the qth state into (q+1)th state. It is worthwhile pointing out that the MIRPSA never goes back to any visited states because the MIRPSA only visits any qth by shrinking operations. In this article, we describe the MIRPSA for mixed integer nonlinear unconstrained optimization problems for doing a deep study of its convergence properties using Markov chain viewpoint. We herein include a low dimension case for showing more details of the MIRPSA, when the algorithm is used for identifying the minimum of a mixed integer quadratic function. Besides, numerical examples are also shown in order to measure the performance of the MIRPSA.

Keywords: direct search, mixed integer optimization, random search, convergence, Markov chain

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Authors: Xinyuan Y. Song, Kai Kang

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Joint models for analyzing longitudinal and survival data are widely used to investigate the relationship between a failure time process and time-variant predictors. A common assumption in conventional joint models in the survival analysis literature is that all predictors are observable. However, this assumption may not always be supported because unobservable traits, namely, latent variables, which are indirectly observable and should be measured through multiple observed variables, are commonly encountered in the medical, behavioral, and financial research settings. In this study, a joint modeling approach to deal with this feature is proposed. The proposed model comprises three parts. The first part is a dynamic factor analysis model for characterizing latent variables through multiple observed indicators over time. The second part is a random coefficient trajectory model for describing the individual trajectories of latent variables. The third part is a proportional hazard model for examining the effects of time-invariant predictors and the longitudinal trajectories of time-variant latent risk factors on hazards of interest. A Bayesian approach coupled with a Markov chain Monte Carlo algorithm to perform statistical inference. An application of the proposed joint model to a study on the Alzheimer's disease neuroimaging Initiative is presented.

Keywords: Bayesian analysis, joint model, longitudinal data, time-to-event data

Procedia PDF Downloads 112

Authors: Parimalah Velo, Ahmad Zakaria

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Introduction: Monte Carlo simulations of preclinical imaging systems allow opportunity to enable new research that could range from designing hardware up to discovery of new imaging application. The simulation system which could accurately model an imaging modality provides a platform for imaging developments that might be inconvenient in physical experiment systems due to the expense, unnecessary radiation exposures and technological difficulties. The aim of present study is to validate the Monte Carlo simulation of thyroid phantom imaging using Geant4-GATE for Siemen’s e-cam single head gamma camera. Upon the validation of the gamma camera simulation model by comparing physical characteristic such as energy resolution, spatial resolution, sensitivity, and dead time, the GATE simulation of thyroid phantom imaging is carried out. Methods: A thyroid phantom is defined geometrically which comprises of 2 lobes with 80mm in diameter, 1 hot spot, and 3 cold spots. This geometry accurately resembling the actual dimensions of thyroid phantom. A planar image of 500k counts with 128x128 matrix size was acquired using simulation model and in actual experimental setup. Upon image acquisition, quantitative image analysis was performed by investigating the total number of counts in image, the contrast of the image, radioactivity distributions on image and the dimension of hot spot. Algorithm for each quantification is described in detail. The difference in estimated and actual values for both simulation and experimental setup is analyzed for radioactivity distribution and dimension of hot spot. Results: The results show that the difference between contrast level of simulation image and experimental image is within 2%. The difference in the total count between simulation and actual study is 0.4%. The results of activity estimation show that the relative difference between estimated and actual activity for experimental and simulation is 4.62% and 3.03% respectively. The deviation in estimated diameter of hot spot for both simulation and experimental study are similar which is 0.5 pixel. In conclusion, the comparisons show good agreement between the simulation and experimental data.

Keywords: gamma camera, Geant4 application of tomographic emission (GATE), Monte Carlo, thyroid imaging

Procedia PDF Downloads 247

Authors: Ming-Yue Zhai

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Due to the multipath and pulse noise nature, power line communications(PLC) channel can be modelled as a memory one with the finite states Markov model(FSMC). As the most important parameter modelling a Markov channel,the memory order in an FSMC is not solved in PLC systems yet. In the paper, the mutual information is used as a measure of the dependence between the different symbols, treated as the received SNA or amplitude of the current channel symbol or that of previous symbols. The joint distribution probabilities of the envelopes in PLC systems are computed based on the multi-path channel model, which is commonly used in PLC. we confirm that given the information of the symbol immediately preceding the current one, any other previous symbol is independent of the current one in PLC systems, which means the PLC channels is a Markov chain with the first-order. The field test is also performed to model the received OFDM signals with the help of AR model. The results show that the first-order AR model is enough to model the fading channel in PLC systems, which means the amount of uncertainty remaining in the current symbol should be negligible, given the information corresponding to the immediately preceding one.

Keywords: power line communication, channel model, markovian, information theory, first-order

Procedia PDF Downloads 379

Authors: Zina Benouaret, Djamil Aissani

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In this work, we introduce the qualitative and quantitative concept of the strong stability method in the risk process modeling two lines of business of the same insurance company or an insurance and re-insurance companies that divide between them both claims and premiums with a certain proportion. The approach proposed is based on the identification of the ruin probability associate to the model considered, with a stationary distribution of a Markov random process called a reversed process. Our objective, after clarifying the condition and the perturbation domain of parameters, is to obtain the stability inequality of the ruin probability which is applied to estimate the approximation error of a model with disturbance parameters by the considered model. In the stability bound obtained, all constants are explicitly written.

Keywords: Markov chain, risk models, ruin probabilities, strong stability analysis

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Authors: Reza Reiazi

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Attempts to verify Geant4 hadronic physic to transport antiproton beam using standard physics list have not reach to a reasonable results because of lack of reliable cross section data or non reliable model to predict the final states of annihilated particles. Since most of the antiproton annihilation energy is carried away by recoiling nuclear fragments which are result of pions interactions with surrounding nucleons, it should be investigated if the toolkit verified for pions. Geant4 version 9.4.6.p01 was used. Dose calculation was done using 700 MeV pions hitting a water tank applying standards physic lists. We conclude Geant4 standard physics lists to predict the depth dose of Pion minus beam is not same for all investigated models. Since the nuclear fragments will deposit their energy in a small distance, they are the most important source of dose deposition in the annihilation vertex of antiproton beams.

Keywords: Monte Carlo, Pion, simulation, antiproton beam

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Authors: Jiemiao Chen, Shuoxun Xu

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The main goal of our research is to apply the Vine copula measuring dependency between price, temperature, and precipitation indices to calculate a fair crop insurance premium. This research is focused on Worth, Iowa, United States, over the period from 2000 to 2020, where the farmers are dependent on precipitation and average temperature during the growth period of corn. Our proposed insurance considers both the natural risk and the price risk in agricultural production. We first estimate the distributions of crops using parametric methods based on Goodness of Fit tests, and then Vine Copula is applied to model dependence between yield price, crop yield, and weather indices. Once the vine structure and its parameters are determined based on AIC/BIC criteria and forecasting price and yield are obtained from the ARIMA model, we calculate this crop insurance premium using the simulation data generated from the vine copula by the Monte Carlo Simulation method. It is shown that, compared with traditional crop insurance, our proposed insurance is more fair and thus less costly for the farmers and government.

Keywords: vine copula, weather index, crop insurance premium, insurance risk management, Monte Carlo simulation

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Authors: François Vallée, Jean-François Toubeau, Zacharie De Grève, Jacques Lobry

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When evaluating the capacity of a generation park to cover the load in transmission systems, traditional Loss of Load Expectation (LOLE) and Expected Energy not Served (EENS) indices can be used. If those indices allow computing the annual duration and severity of load non-covering situations, they do not take into account the fact that the load excess is generally shifted from one penury state (hour or quarter of an hour) to the following one. In this paper, a sequential Monte Carlo framework is introduced in order to compute adjusted LOLE and EENS indices. Practically, those adapted indices permit to consider the effect of load excess transfer on the global adequacy of a generation park, providing thus a more accurate evaluation of this quantity.

Keywords: expected energy not served, loss of load expectation, Monte Carlo simulation, reliability, wind generation

Procedia PDF Downloads 375

Authors: Fang Ye, Han Yu, Yibing Li

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Radio interference is one of the major concerns in using the global positioning system (GPS) for civilian and military applications. Interference signals are produced not only through all electronic systems but also illegal jammers. Among different types of interferences, continuous wave (CW) interference has strong adverse impacts on the quality of the received signal. In this paper, we make more detailed analysis for CW interference effects on GPS signal quality. Based on the C/A code spectrum lines, the influence of CW interference on the acquisition performance of GPS receivers is further analysed. This influence is supported by simulation results using GPS software receiver. As the most important user parameter of GPS receivers, the mathematical expression of bit error probability is also derived in the presence of CW interference, and the expression is consistent with the Monte Carlo simulation results. The research on CW interference provides some theoretical gist and new thoughts on monitoring the radio noise environment and improving the anti-jamming ability of GPS receivers.

Keywords: GPS, CW interference, acquisition performance, bit error probability, Monte Carlo

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Authors: Maurizio Dapor, Isabel Abril, Pablo de Vera, Rafael Garcia-Molina

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The ionization yield of ion tracks in polymers and bio-molecular systems reaches a maximum, known as the Bragg peak, close to the end of the ion trajectories. Along the path of the ions through the materials, many electrons are generated, which produce a cascade of further ionizations and, consequently, a shower of secondary electrons. Among these, very low energy secondary electrons can produce damage in the biomolecules by dissociative electron attachment. This work deals with the calculation of the energy distribution of electrons produced by protons in a sample of polymethylmethacrylate (PMMA), a material that is used as a phantom for living tissues in hadron therapy. PMMA is also of relevance for microelectronics in CMOS technologies and as a photoresist mask in electron beam lithography. We present a Monte Carlo code that, starting from a realistic description of the energy distribution of the electrons ejected by protons moving through PMMA, simulates the entire cascade of generated secondary electrons. By following in detail the motion of all these electrons, we find the radial distribution of the energy that they deposit in PMMA for several initial proton energies characteristic of the Bragg peak.

Keywords: Monte Carlo method, secondary electrons, energetic ions, ion-beam cancer therapy, ionization cross section, polymethylmethacrylate, proton beams, secondary electrons, radial energy distribution

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Authors: Mohammed El-Arbi Khalfallah, Mohamed Lakhdar Hadji

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Managing and controlling risk is a topic research in the world of finance. Before a risky situation, the stakeholders need to do comparison according to the positions and actions, and financial institutions must take measures of a particular market risk and credit. In this work, we study a model of risk measure in finance: Value at Risk (VaR), which is a new tool for measuring an entity's exposure risk. We explain the concept of value at risk, your average, tail, and describe the three methods for computing: Parametric method, Historical method, and numerical method of Monte Carlo. Finally, we briefly describe advantages and disadvantages of the three methods for computing value at risk.

Keywords: average value at risk, conditional value at risk, tail value at risk, value at risk

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Authors: Gak-Gyu Kim, Won Il Jung

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According to a remarkable development of science and technology, function and role of the system of engineering fields has recently been diversified. The system has become increasingly more complex and precise, and thus, system designers intended to maximize reliability concentrate more effort at the design stage. This study deals with the reliability redundancy optimization problem (RROP) for k-out-of-n: G system configuration with cold standby and warm standby components. This paper further intends to present the optimal mathematical model through which the following three elements of (i) multiple components choices, (ii) redundant components quantity and (iii) the choice of redundancy strategies may be combined in order to maximize the reliability of the system. Therefore, we focus on the following three issues. First, we consider RROP that there exists warm standby state as well as cold standby state of the component. Second, as eliminating an approximation approach of the previous RROP studies, we construct a precise model for system reliability. Third, given transition time when the state of components changes, we present not simply a workable solution but the advanced method. For the wide applicability of RROPs, moreover, we use absorbing continuous time Markov chain and matrix analytic methods in the suggested mathematical model.

Keywords: RROP, matrix analytic methods, k-out-of-n: G system, MTTF, absorbing continuous time Markov Chain

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Authors: Balasundaram Prasaant, Ploix Stephane, Delinchant Benoit, Muresan Cristian

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Hardware in Loop (HIL) testing is done to test and validate a particular product especially in building technology. When it comes to building technology, it is more important to test the products for their efficiency. The test rig in the HIL simulator may contribute to some uncertainties on measured efficiency. The uncertainties include physical uncertainties and scenario-based uncertainties. In this paper, a simple uncertainty analysis framework for an HIL setup is shown considering only the physical uncertainties. The entire modeling of the HIL setup is done in Dymola. The uncertain sources are considered based on available knowledge of the components and also on expert knowledge. For the propagation of uncertainty, Monte Carlo Simulation is used since it is the most reliable and easy to use. In this article it is shown how an HIL setup can be modeled and how uncertainty propagation can be performed on it. Such an approach is not common in building energy analysis.

Keywords: energy in buildings, hardware in loop testing, modelica modelling, Monte Carlo simulation, uncertainty propagation

Procedia PDF Downloads 106

Authors: Ali Mohaghar, Iman Ghasemian Sahebi, Alireza Arab

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In the last decades, increasing in human and natural disaster occurrence had very irreparable effects on human life. Hence, one of the important issues in humanitarian supply chain management is identifying and prioritizing the different risks and finding suitable solutions for encountering them at the time of disaster occurrence. This study is an attempt to provide a comprehensive review of humanitarian supply chain risks in a case study of Tehran Red Crescent Societies. For this purpose, Best-Worst method (BWM) has been used for analyzing the risks of the humanitarian supply chain. 22 risks of the humanitarian supply chain were identified based on the literature and interviews with four experts. According to BWM method, the importance of each risk was calculated. The findings showed that culture contexts, little awareness of people, and poor education system are the most important humanitarian supply chain risks. This research provides a useful guideline for managers so that they can benefit from the results to prioritize their solutions.

Keywords: Best-Worst Method, humanitarian logistics, humanitarian supply chain, risk management

Procedia PDF Downloads 284

Authors: Diep Thanh Tung, Joachim Aurbacher

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This study aims to measure the level of on-farm diversification in Vietnam. The empirical results of the research carried out reflect regional differences in terms of on-farm diversification and its determinants. Households in the northern regions have adapted to the fragmented and small-sized parcels of land held by diversifying their on-farm activities. In contrast, the Mekong delta region in the south of Vietnam is characterized by larger agricultural parcels and a specialization in rice production. Land use fragmentation, as reflected by a large number of plots in a given area, is one of the most important reasons for the high levels of on-farm diversification seen, while the higher share of non-farm income in total income is the reason of lower levels of on-farm diversification. Households have reacted to natural and economic shocks by diversifying their on-farm activities. The non-stationary Markov chain model used here shows various diversification scenarios and trends. In most cases, on-farm diversification generally tends to reduce over the next few years.

Keywords: diversification, simpson index, fixed effects, non-stationary markov chain

Procedia PDF Downloads 459

Authors: Sidi Mohamed Mesli, Mohamed Habchi, Arslane Boudghene Stambouli, Rafik Benallal

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The BaMnMF7 (M=Fe,V, transition metal fluoride glass, assuming isomorphous replacement) have been structurally studied through the simultaneous simulation of their neutron diffraction patterns by reverse Monte Carlo (RMC) and by the Hybrid Reverse Monte Carlo (HRMC) analysis. This last is applied to remedy the problem of the artificial satellite peaks that appear in the partial pair distribution functions (PDFs) by the RMC simulation. The HRMC simulation is an extension of the RMC algorithm, which introduces an energy penalty term (potential) in acceptance criteria. The idea of this work is to apply the Buckingham potential at the title glass by ignoring the van der Waals terms, in order to make a fit of the partial pair distribution functions and give the most possible realistic features. When displaying the partial PDFs, we suggest that the Buckingham potential is useful to describe average correlations especially in similar interactions.

Keywords: fluoride glasses, RMC simulation, hybrid RMC simulation, Buckingham potential, partial pair distribution functions

Procedia PDF Downloads 475

Authors: H. Mousavi, M. Sharifi, H. Pourvaziri

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Redundancy Allocation Problem (RAP) is a well-known mathematical problem for modeling series-parallel systems. It is a combinatorial optimization problem which focuses on determining an optimal assignment of components in a system design. In this paper, to be more practical, we have considered the problem of redundancy allocation of series system with interval valued reliability of components. Therefore, during the search process, the reliabilities of the components are considered as a stochastic variable with a lower and upper bounds. In order to optimize the problem, we proposed a simulated annealing based on cloud theory (CBSAA). Also, the Monte Carlo simulation (MCS) is embedded to the CBSAA to handle the random variable components’ reliability. This novel approach has been investigated by numerical examples and the experimental results have shown that the CBSAA combining MCS is an efficient tool to solve the RAP of systems with interval-valued component reliabilities.

Keywords: redundancy allocation problem, simulated annealing, cloud theory, monte carlo simulation

Procedia PDF Downloads 387

Authors: Αlexios Kotsakis, Stefanos Katsavounis, Dimitra Alexiou

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Networks can be utilized to represent project planning problems, using nodes for activities and arcs to indicate precedence relationship between them. For fixed activity duration, a simple algorithm calculates the amount of time required to complete a project, followed by the activities that comprise the critical path. Program Evaluation and Review Technique (PERT) generalizes the above model by incorporating uncertainty, allowing activity durations to be random variables, producing nevertheless a relatively crude solution in planning problems. In this paper, based on the findings of the relevant literature, which strongly suggests that a Beta distribution can be employed to model earthmoving activities, we utilize Monte Carlo simulation, to estimate the project completion time distribution and measure the influence of skewness, an element inherent in activities of modern technical projects. We also extract the activity criticality index, with an ultimate goal to produce more accurate planning estimations.

Keywords: beta distribution, PERT, Monte Carlo simulation, skewness, project completion time distribution

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Authors: Kalu Ram Meena, Aditi Kar Gangopadhyay, Satrajit Mandal

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Two normal populations with different means and same variance are considered, where the variances are known. The population with the smaller sample mean is selected. Various estimators are constructed for the mean of the selected normal population. Finally, they are compared with respect to the bias and MSE risks by the method of Monte-Carlo simulation and their performances are analysed with the help of graphs.

Keywords: estimation after selection, Brewster-Zidek technique, estimators, selected populations

Procedia PDF Downloads 476

Authors: Safia Hocine, Zina Benouaret, Djamil A¨ıssani

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In this paper, we study the performance of the strong stability method of the univariate classical risk model. We interest to the stability bounds established using two approaches. The first based on the strong stability method developed for a general Markov chains. The second approach based on the regenerative processes theory . By adopting an algorithmic procedure, we study the performance of the stability method in the case of exponential distribution claim amounts. After presenting numerically and graphically the stability bounds, an interpretation and comparison of the results have been done.

Keywords: Marcov chain, regenerative process, risk model, ruin probability, strong stability

Procedia PDF Downloads 288

Authors: Keunock Lew, Seungryul Ma

Abstract:

The paper analyzes risk and expected return of lenders who provide mortgage loans to households in the shared mortgage program of Korea. In 2013, the Korean government introduced the mortgage program to help low income householders to convert their renting into purchasing houses. The financial source for the mortgage program is the Urban Housing Fund set up by the Korean government. Through the program, low income households can borrow money from lenders to buy a house at a very low interest rate (e.g. 1 % per year) for a long time. The motivation of adopting this mortgage program by the Korean government is that the cost of renting houses has been rapidly increased especially in large urban areas during the past decade, which became financial difficulties to low income households who do not have their own houses. As the analysis methodology, the paper uses a spread sheet model for projecting cash flows of the mortgage product over the period of loan contract. It also employs Monte Carlo simulation method to analyze the risk and expected yield of the lenders with assumption that the future housing price and market rate of interest follow a stochastic process. The study results will give valuable implications to the Korean government and lenders who want to stabilize the mortgage program and innovate the related loan products.

Keywords: expected return, Monte Carlo simulation, risk, shared mortgage program

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Authors: Isabela Moreira Queiroz

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Slope stability analyses are largely carried out by deterministic methods and evaluated through a single security factor. Although it is known that the geotechnical parameters can present great dispersal, such analyses are considered fixed and known. The probabilistic methods, in turn, incorporate the variability of input key parameters (random variables), resulting in a range of values of safety factors, thus enabling the determination of the probability of failure, which is an essential parameter in the calculation of the risk (probability multiplied by the consequence of the event). Among the probabilistic methods, there are three frequently used methods in geotechnical society: FOSM (First-Order, Second-Moment), Rosenblueth (Point Estimates) and Monte Carlo. This paper presents a comparison between the results from deterministic and probabilistic analyses (FOSM method, Monte Carlo and Rosenblueth) applied to a hypothetical slope. The end was held to evaluate the behavior of the slope and consequent risk analysis, which is used to calculate the risk and analyze their mitigation and control solutions. It can be observed that the results obtained by the three probabilistic methods were quite close. It should be noticed that the calculation of the risk makes it possible to list the priority to the implementation of mitigation measures. Therefore, it is recommended to do a good assessment of the geological-geotechnical model incorporating the uncertainty in viability, design, construction, operation and closure by means of risk management. 

Keywords: probabilistic methods, risk assessment, risk management, slope stability

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Authors: Asavasereerat K., Teacharsripaitoon T., Tungyingyong P., Charupongrat S., Noppiboon S. Hochareon L., Kitsuban P.

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Third generation of vaccines is based on gene therapy where DNA is introduced into patients. The antigenic or therapeutic proteins encoded from transgenes DNA triggers an immune-response to counteract various diseases. Moreover, DNA vaccine offers the customization of its ability on protection and treatment with high stability. The production of DNA vaccines become of interest. According to USFDA guidance for industry, the recommended limits for impurities from host cell are lower than 1%, and the active conformation homogeneity supercoiled DNA, is more than 80%. Thus, the purification strategy using two-steps chromatography has been established and verified for its robustness. Herein, pVax1/lacZ, a pre-approved USFDA DNA vaccine backbone, was used and transformed into E. coli strain DH5α. Three purification process parameters including sample-loading flow rate, the salt concentration in washing and eluting buffer, were studied and the experiment was designed using response surface method with central composite face-centered (CCF) as a model. The designed range of selected parameters was 10% variation from the optimized set point as a safety factor. The purity in the percentage of supercoiled conformation obtained from each chromatography step, AIEX and HIC, were analyzed by HPLC. The response data were used to establish regression model and statistically analyzed followed by Monte Carlo simulation using SAS JMP. The results on the purity of the product obtained from AIEX and HIC are between 89.4 to 92.5% and 88.3 to 100.0%, respectively. Monte Carlo simulation showed that the pVAX1/lacZ purification process is robust with confidence intervals of 0.90 in range of 90.18-91.00% and 95.88-100.00%, for AIEX and HIC respectively.

Keywords: AIEX, DNA vaccine, HIC, puification, response surface method, robustness

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Authors: Omar Alzeley, Sergey Utev

Abstract:

Minimizing a constrained multivariate function is the fundamental of Machine learning, and these algorithms are at the core of data mining and data visualization techniques. The decision function that maps input points to output points is based on the result of optimization. This optimization is the central of learning theory. One approach to complex systems where the dynamics of the system is inferred by a statistical analysis of the fluctuations in time of some associated observable is time series analysis. The purpose of this paper is a mathematical transition from the autoregressive model of classical time series to the matrix formalization of quantum theory. Firstly, we have proposed a quantum time series model (QTS). Although Hamiltonian technique becomes an established tool to detect a deterministic chaos, other approaches emerge. The quantum probabilistic technique is used to motivate the construction of our QTS model. The QTS model resembles the quantum dynamic model which was applied to financial data. Secondly, various statistical methods, including machine learning algorithms such as the Kalman filter algorithm, are applied to estimate and analyses the unknown parameters of the model. Finally, simulation techniques such as Markov chain Monte Carlo have been used to support our investigations. The proposed model has been examined by using real and simulated data. We establish the relation between quantum statistical machine and quantum time series via random matrix theory. It is interesting to note that the primary focus of the application of QTS in the field of quantum chaos was to find a model that explain chaotic behaviour. Maybe this model will reveal another insight into quantum chaos.

Keywords: machine learning, simulation techniques, quantum probability, tensor product, time series

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Authors: Yo Fukutani, Shuji Moriguchi, Takuma Kotani, Terada Kenjiro

Abstract:

If risk management of the assets owned by companies, risk assessment of real estate portfolio, and risk identification of the entire region are to be implemented, it is necessary to consider simultaneous damage to multiple buildings. In this research, the Sagami Trough earthquake tsunami that could have a significant effect on the Japanese capital region is focused on, and a method is proposed for simultaneous damage assessment using copulas that can take into consideration the correlation of tsunami depths and building damage between two sites. First, the tsunami inundation depths at two sites were simulated by using a nonlinear long-wave equation. The tsunamis were simulated by varying the slip amount (five cases) and the depths (five cases) for each of 10 sources of the Sagami Trough. For each source, the frequency distributions of the tsunami inundation depth were evaluated by using the response surface method. Then, Monte-Carlo simulation was conducted, and frequency distributions of tsunami inundation depth were evaluated at the target sites for all sources of the Sagami Trough. These are marginal distributions. Kendall’s tau for the tsunami inundation simulation at two sites was 0.83. Based on this value, the Gaussian copula, t-copula, Clayton copula, and Gumbel copula (n = 10,000) were generated. Then, the simultaneous distributions of the damage rate were evaluated using the marginal distributions and the copulas. For the correlation of the tsunami inundation depth at the two sites, the expected value hardly changed compared with the case of no correlation, but the damage rate of the ninety-ninth percentile value was approximately 2%, and the maximum value was approximately 6% when using the Gumbel copula.

Keywords: copulas, Monte-Carlo simulation, probabilistic risk assessment, tsunamis

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Authors: Shih-Kuei Lin

Abstract:

The characterization of the arbitrage-free dynamics of interest rates is developed in this study under the presence of Markov jump risks, when the term structure of the interest rates is modeled through simple forward rates. We consider Markov jump risks by allowing randomness in jump sizes, independence between jump sizes and jump times. The Markov jump diffusion model is used to capture empirical phenomena and to accurately describe interest jump risks in a financial market. We derive the arbitrage-free model of simple forward rates under the spot measure. Moreover, the analytical pricing formulas for a cap and a floor are derived under the forward measure when the jump size follows a lognormal distribution. In our empirical analysis, we find that the LIBOR market model with Markov jump risk better accounts for changes from/to different states and different rates.

Keywords: arbitrage-free, cap and floor, Markov jump diffusion model, simple forward rate model, volatility smile, EM algorithm

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Authors: S. Phanyaem

Abstract:

This paper presents the confidence intervals for the effect size base on bootstrap resampling method. The meta-analytic confidence interval for effect size is proposed that are easy to compute. A Monte Carlo simulation study was conducted to compare the performance of the proposed confidence intervals with the existing confidence intervals. The best confidence interval method will have a coverage probability close to 0.95. Simulation results have shown that our proposed confidence intervals perform well in terms of coverage probability and expected length.

Keywords: effect size, confidence interval, bootstrap method, resampling

Procedia PDF Downloads 566

Authors: Pabitra Sahu, M. Manohari, S. Priyadharshini, R. Santhanam, S. Chandrasekaran, B. Venkatraman

Abstract:

Radiation workers of reprocessing plants have a potential for internal exposure due to actinides and fission products. Radionuclides like Americium, lead, Polonium and Europium are bone seekers and get accumulated in the skeletal part. As the major skeletal content is in the skull (13%) and knee (22%), measurements of old intake have to be carried out in the skull and knee. At the Indira Gandhi Centre for Atomic Research, a twin HPGe-based actinide monitor is used for the measurement of actinides present in bone. Efficiency estimation, which is one of the prerequisites for the quantification of radionuclides, requires anthropomorphic phantoms. Such phantoms are very limited. Hence, in this study, efficiency curves for a Twin HPGe-based actinide monitoring system are established theoretically using the FLUKA Monte Carlo method and ICRP adult male voxel phantom. In the case of skull measurement, the detector is placed over the forehead, and for knee measurement, one detector is placed over each knee. The efficiency values of radionuclides present in the knee and skull vary from 3.72E-04 to 4.19E-04 CPS/photon and 5.22E-04 to 7.07E-04 CPS/photon, respectively, for the energy range 17 to 3000keV. The efficiency curves for the measurement are established, and it is found that initially, the efficiency value increases up to 100 keV and then starts decreasing. It is found that the skull efficiency values are 4% to 63% higher than that of the knee, depending on the energy for all the energies except 17.74 keV. The reason is the closeness of the detector to the skull compared to the knee. But for 17.74 keV the efficiency of the knee is more than the skull due to the higher attenuation caused in the skull bones because of its greater thickness. The Minimum Detectable Activity (MDA) for 241Am present in the skull and knee is 9 Bq. 239Pu has a MDA of 950 Bq and 1270 Bq for knee and skull, respectively, for a counting time of 1800 sec. This paper discusses the simulation method and the results obtained in the study.

Keywords: FLUKA Monte Carlo Method, ICRP adult male voxel phantom, knee, Skull.

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