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Journal articles on the topic 'Population Monte Carlo'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 2023

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1

Cappé, O., A. Guillin, J. M. Marin, and C. P. Robert. "Population Monte Carlo." Journal of Computational and Graphical Statistics 13, no. 4 (December 2004): 907–29. http://dx.doi.org/10.1198/106186004x12803.

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2

Iba, Yukito. "Population Monte Carlo algorithms." Transactions of the Japanese Society for Artificial Intelligence 16 (2001): 279–86. http://dx.doi.org/10.1527/tjsai.16.279.

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3

El-Laham, Yousef, and Monica F. Bugallo. "Stochastic Gradient Population Monte Carlo." IEEE Signal Processing Letters 27 (2020): 46–50. http://dx.doi.org/10.1109/lsp.2019.2954048.

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4

Griffiths, R. C., and S. Tavaré. "Monte Carlo inference methods in population genetics." Mathematical and Computer Modelling 23, no. 8-9 (April 1996): 141–58. http://dx.doi.org/10.1016/0895-7177(96)00046-5.

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5

Lee, Jeong Eun, Ross McVinish, and Kerrie Mengersen. "Population Monte Carlo Algorithm in High Dimensions." Methodology and Computing in Applied Probability 13, no. 2 (August 26, 2009): 369–89. http://dx.doi.org/10.1007/s11009-009-9154-2.

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6

Miller, Caleb, Jem N. Corcoran, and Michael D. Schneider. "Rare Events via Cross-Entropy Population Monte Carlo." IEEE Signal Processing Letters 29 (2022): 439–43. http://dx.doi.org/10.1109/lsp.2021.3139572.

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7

GONZÁLEZ-PARRA, GILBERTO, ABRAHAM J. ARENAS, and F. J. SANTONJA. "STOCHASTIC MODELING WITH MONTE CARLO OF OBESITY POPULATION." Journal of Biological Systems 18, no. 01 (March 2010): 93–108. http://dx.doi.org/10.1142/s0218339010003159.

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In this paper, we investigate the dynamics of a mathematical model of obesity population within fluctuating social environment. A stochastic differential equation model is constructed by perturbing two social related parameters of the deterministic model with white noise terms characterized by Gaussian distribution having zero mean and unit spectral density. In order to compute the numerical solution of the stochastic models Euler-Maruyama numerical method is used. Confidence intervals for the overweight and obesity childhood population are computed using Monte Carlo method. Analysis of the numerical results reveals that small perturbations on the parameters are not a major driving force for dynamical transitions from the underlying deterministic model. In addition, numerical results indicate a close relationship between the amplitude of the fluctuation of the social environment parameters and the variability of forecasts for the incidence of the obesity in the population.

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8

Smith, Matthew, and Themis Matsoukas. "Constant-number Monte Carlo simulation of population balances." Chemical Engineering Science 53, no. 9 (May 1998): 1777–86. http://dx.doi.org/10.1016/s0009-2509(98)00045-1.

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9

Legrady, David, Mate Halasz, Jozsef Kophazi, Balazs Molnar, and Gabor Tolnai. "Population-based variance reduction for dynamic Monte Carlo." Annals of Nuclear Energy 149 (December 2020): 107752. http://dx.doi.org/10.1016/j.anucene.2020.107752.

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10

Jasra, A., D. A. Stephens, and C. C. Holmes. "Population-Based Reversible Jump Markov Chain Monte Carlo." Biometrika 94, no. 4 (August 5, 2007): 787–807. http://dx.doi.org/10.1093/biomet/asm069.

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11

Pflaumer, Peter. "Forecasting the German population with Monte Carlo methods." Economics Letters 21, no. 4 (January 1986): 385–90. http://dx.doi.org/10.1016/0165-1765(86)90209-0.

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12

Nielsen, Rasmus, and John Wakeley. "Distinguishing Migration From Isolation: A Markov Chain Monte Carlo Approach." Genetics 158, no. 2 (June 1, 2001): 885–96. http://dx.doi.org/10.1093/genetics/158.2.885.

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Abstract A Markov chain Monte Carlo method for estimating the relative effects of migration and isolation on genetic diversity in a pair of populations from DNA sequence data is developed and tested using simulations. The two populations are assumed to be descended from a panmictic ancestral population at some time in the past and may (or may not) after that be connected by migration. The use of a Markov chain Monte Carlo method allows the joint estimation of multiple demographic parameters in either a Bayesian or a likelihood framework. The parameters estimated include the migration rate for each population, the time since the two populations diverged from a common ancestral population, and the relative size of each of the two current populations and of the common ancestral population. The results show that even a single nonrecombining genetic locus can provide substantial power to test the hypothesis of no ongoing migration and/or to test models of symmetric migration between the two populations. The use of the method is illustrated in an application to mitochondrial DNA sequence data from a fish species: the threespine stickleback (Gasterosteus aculeatus).

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13

García-Berro, E., S. Torres, J. Isern, and A. Burkert. "Monte Carlo simulations of the halo white dwarf population." Astronomy & Astrophysics 418, no. 1 (April 2004): 53–65. http://dx.doi.org/10.1051/0004-6361:20034541.

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14

Garcia-Berro, E., S. Torres, J. Isern, and A. Burkert. "Monte Carlo simulations of the disc white dwarf population." Monthly Notices of the Royal Astronomical Society 302, no. 1 (January 1, 1999): 173–88. http://dx.doi.org/10.1046/j.1365-8711.1999.02115.x.

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15

Chi, Hongmei, and Peter Beerli. "Quasi-Monte Carlo method in population genetics parameter estimation." Mathematics and Computers in Simulation 103 (September 2014): 33–38. http://dx.doi.org/10.1016/j.matcom.2014.02.005.

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16

Elvira, Víctor, Luca Martino, David Luengo, and Mónica F. Bugallo. "Improving population Monte Carlo: Alternative weighting and resampling schemes." Signal Processing 131 (February 2017): 77–91. http://dx.doi.org/10.1016/j.sigpro.2016.07.012.

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17

Lai, Yu-Chi, Stephen Chenney, Feng Liu, Yuzhen Niu, and Shaohua Fan. "Animation rendering with Population Monte Carlo image-plane sampler." Visual Computer 26, no. 6-8 (April 17, 2010): 543–53. http://dx.doi.org/10.1007/s00371-010-0503-5.

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18

Finke, Axel, Ruth King, Alexandros Beskos, and Petros Dellaportas. "Efficient Sequential Monte Carlo Algorithms for Integrated Population Models." Journal of Agricultural, Biological and Environmental Statistics 24, no. 2 (January 23, 2019): 204–24. http://dx.doi.org/10.1007/s13253-018-00349-9.

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19

Cerf, Nicolas, and Olivier C. Martin. "Finite population-size effects in projection Monte Carlo methods." Physical Review E 51, no. 4 (April 1, 1995): 3679–93. http://dx.doi.org/10.1103/physreve.51.3679.

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20

Charlebois, Daniel A., and Mads Kærn. "An Accelerated Method for Simulating Population Dynamics." Communications in Computational Physics 14, no. 2 (August 2013): 461–76. http://dx.doi.org/10.4208/cicp.130612.121012a.

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AbstractWe present an accelerated method for stochastically simulating the dynamics of heterogeneous cell populations. The algorithm combines a Monte Carlo approach for simulating the biochemical kinetics in single cells with a constant-number Monte Carlo method for simulating the reproductive fitness and the statistical characteristics of growing cell populations. To benchmark accuracy and performance, we compare simulation results with those generated from a previously validated population dynamics algorithm. The comparison demonstrates that the accelerated method accurately simulates population dynamics with significant reductions in runtime under commonly invoked steady-state and symmetric cell division assumptions. Considering the increasing complexity of cell population models, the method is an important addition to the arsenal of existing algorithms for simulating cellular and population dynamics that enables efficient, coarse-grained exploration of parameter space.

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21

Fish, Laurel J., Dennis Halcoussis, and G. Michael Phillips. "Statistical Analysis Of A Class: Monte Carlo And Multiple Imputation Spreadsheet Methods For Estimation And Extrapolation." American Journal of Business Education (AJBE) 10, no. 2 (March 31, 2017): 81–96. http://dx.doi.org/10.19030/ajbe.v10i2.9918.

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The Monte Carlo method and related multiple imputation methods are traditionally used in math, physics and science to estimate and analyze data and are now becoming standard tools in analyzing business and financial problems. However, few sources explain the application of the Monte Carlo method for individuals and business professionals who are not immersed in the realm of mathematics or science. This paper introduces these Monte Carlo methods for the non-mathematician and business student, providing examples where the Monte Carlo method is applied when only small samples are available. Statistical analysis and statistically sound extrapolation of sample characteristics to the larger class population can be facilitated by applying Monte Carlo methods and the related concept of multiple imputation, which is also explained. Appendices provide step-by-step instructions for using two popular spreadsheet add-ins to run Monte Carlo based analysis.

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22

CEBRAT, STANISŁAW, ANDRZEJ PȨKALSKI, and FABIAN SCHARF. "MONTE CARLO SIMULATIONS OF THE INSIDE INTRON RECOMBINATION." International Journal of Modern Physics C 17, no. 02 (February 2006): 305–14. http://dx.doi.org/10.1142/s0129183106008984.

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Biological genomes are divided into coding and non-coding regions. Introns are non-coding parts within genes, while the remaining non-coding parts are intergenic sequences. To study evolutionary significance of the inside intron recombination we have used two models based on the Monte Carlo method. In our computer simulations we have implemented the internal structure of genes by declaring the probability of recombination between exons. One situation when inside intron recombination is advantageous is recovering functional genes by combining proper exons dispersed in the genetic pool of the population after a long period without selection for the function of the gene. Populations have to pass through the bottleneck, then. These events are rather rare and we have expected that there should be other phenomena giving profits from the inside intron recombination. In fact we have found that inside intron recombination is advantageous only in the case when after recombination, besides the recombinant forms, parental haplotypes are available and selection is set already on gametes.

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23

Andrews, Jeff J., Andreas Zezas, and Tassos Fragos. "dart _ board: Binary Population Synthesis with Markov Chain Monte Carlo." Astrophysical Journal Supplement Series 237, no. 1 (July 2, 2018): 1. http://dx.doi.org/10.3847/1538-4365/aaca30.

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24

Dufek, Jan, and Kaur Tuttelberg. "Monte Carlo criticality calculations accelerated by a growing neutron population." Annals of Nuclear Energy 94 (August 2016): 16–21. http://dx.doi.org/10.1016/j.anucene.2016.02.015.

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25

Mickus, Ignas, and Jan Dufek. "Optimal neutron population growth in accelerated Monte Carlo criticality calculations." Annals of Nuclear Energy 117 (July 2018): 297–304. http://dx.doi.org/10.1016/j.anucene.2018.03.046.

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26

Pflaumer, Peter. "Confidence intervals for population projections based on Monte Carlo methods." International Journal of Forecasting 4, no. 1 (January 1988): 135–42. http://dx.doi.org/10.1016/0169-2070(88)90015-5.

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27

Xu, Zuwei, Haibo Zhao, and Chuguang Zheng. "Fast Monte Carlo simulation for particle coagulation in population balance." Journal of Aerosol Science 74 (August 2014): 11–25. http://dx.doi.org/10.1016/j.jaerosci.2014.03.006.

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28

Sá Martins, J. S., and S. Moss de Oliveira. "Why Sex? — Monte Carlo Simulations of Survival after Catastrophes." International Journal of Modern Physics C 09, no. 03 (May 1998): 421–32. http://dx.doi.org/10.1142/s0129183198000327.

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Using the Penna bit-string model for biological ageing we compare two kinds of reproductive regimes: Sexual reproduction (SR) and meiotic parthenogenesis (MP). The last one is a common type of asexual reproduction with recombination, found in diploid organisms. We show that although both regimes present roughly the same survival rates, the diversity generated by SR is much larger, and can prevent the extinction of a population submitted to a natural disaster. The fixation of bad genes inside an MP population, after many generations, explains our results. We also study the consequences of cloning (simple copy) on population diversity.

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29

Mielenz, N., M. Wensch-Dorendorf, and L. Schüler. "Optimierung von Zuchtstrukturen einer Zweilinienkreuzung – Eine Monte-Carlo Studie." Archives Animal Breeding 46, no. 3 (October 10, 2003): 293–303. http://dx.doi.org/10.5194/aab-46-293-2003.

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Abstract. Title of the paper: Optimization of the population structure for a two-line crossbreeding scheme – a Monte-Carlo study A stochastic simulation was used to optimize the population structure in a two-line crossbreeding system under non-additive gene models. For different fixed test capacities, given number of offspring per dam and varying degrees of dominance the optimum number of selected sires was calculated. As criterions of the optimization the cumulative selection response on generation 10, the corresponding standard error of the response and the development of the inbreeding in the purebreds were used. If the trait was controlled by loci with partial and complete dominance, than the optimal number of selected sires was between 8 and 12 for given test capacities of 2304, 1152 and 1536 animals per generation and per the sire line. In the case of overdominance the number of selected sires increased on 24 to 48. Additionally, the level of the selection response in the three populations was affected by the difference of the allele frequencies in the initial generation and the economic weights of the additive purebred and crossbred effects. Under partial and complete dominance with corresponding purebred-crossbred genetic correlations of high and moderate level only small extra benefits were obtained from including crossbred information over pure line information.

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30

HE, MINGFENG, CHANGLIANG YU, HONGBO RUAN, and LEI YAO. "EVOLUTION OF POPULATION WITH SEXUAL AND ASEXUAL REPRODUCTION IN CHANGING ENVIRONMENT." International Journal of Modern Physics C 15, no. 02 (February 2004): 289–99. http://dx.doi.org/10.1142/s0129183104005693.

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Using a lattice model based on Monte Carlo simulations, we study the role of the reproduction pattern on the fate of an evolving population. Each individual is under the selection pressure from the environment and random mutations. The habitat ("climate") is changing periodically. Evolutions of populations following two reproduction patterns are compared, asexual and sexual. We show, via Monte Carlo simulations, that sexual reproduction by keeping more diversified populations gives them better chances to adapt themselves to the changing environment. However, in order to obtain a greater chance to mate, the birth rate should be high. In the case of low birth rate and high mutation probability there is a preference for the asexual reproduction.

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31

Dewey, Rachel J., and James M. Cordes. "Monte Carlo Simulations of Radio Pulsars and Their Progenitors." Symposium - International Astronomical Union 125 (1987): 408. http://dx.doi.org/10.1017/s0074180900161030.

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The formation of neutron stars in binary systems is often used to explain the nature of specific radio pulsars and characteristics of the pulsar population as a whole. We have investigated the extent to which such scenarios provide a self-consistent description of the pulsar population. Using a computer simulation, we modeled the evolution of the main sequence stellar population and compared the predicted neutron star population to the observed radio pulsar population, focusing our attention on the pulsar velocity distribution and the incidence of binary pulsars. These characteristics relate very directly to the binary nature of pulsar progenitors, and are not strongly dependent on models of pulsar magentic field and luminosity evolution.The need to reproduce both the high velocities typical of pulsars and the low incidence of binary pulsars strongly constrains the formation of pulsars in binary systems. Unless one assumes that virtually all pulsars originate in close binary systems, the observed velocity distribution cannot result from the disruption of binary systems by symmetric supernova explosions; some additional acceleration process (e.g. asymmetric supernova mass ejection or asymmetries in pulsar radiation) must act during or soon after a pulsar's formation. It is possible to reproduce the velocity distribution by assuming that all pulsars are born in binary systems with initial orbital periods less than about 30 years. However, the predicted incidence of binaries is then too large by more than an order of magnitude, unless one also assumes that the process of mass transfer from the primary to the secondary is almost always non-conservative, or that the minimum mass necessary for a stripped helium core to explode as a supernova is larger (over 4 M⊙) than currently believed. Further analyses of the radio pulsar population, the X-ray binary population and the abundances of elements ejected in supernovae should help determine which of these alternatives is most reasonble. Additional studies of the main sequence stellar population, accounting more accurately for evolutionary and observational selection effects, will reduce the uncertainties in modeling the formation of the neutron star population.It has also been suggested that the observed correlation between pulsar velocities and magnetic moments (see Cordes, these Proceedings) is induced by the differing evolutionary paths by which stars in binary systems form radio pulsars. Our simulation does not reproduce this correlation, and we do not find any paths likely to produce low velocity, low magnetic field neutron stars not in binary systems.We are submitting a full description of our model and results to The Astrophysical Journal.

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32

CERF, NICOLAS J., and OLIVIER C. MARTIN. "PROJECTION MONTE CARLO METHODS: AN ALGORITHMIC ANALYSIS." International Journal of Modern Physics C 06, no. 05 (October 1995): 693–723. http://dx.doi.org/10.1142/s0129183195000587.

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Projection methods such as Green's function and diffusion Monte Carlo are commonly used to calculate the leading eigenvalue and eigenvector of operators or large matrices. They thereby give access to ground state properties of quantum systems, and finite temperature properties of classical statistical mechanical systems having a transfer matrix. The basis of these approaches is a stochastic application of the power method in which a "projection" operator is applied iteratively. For the systematic errors to be small, the number of iterations must be large; however, in that limit, the statistical errors grow tremendously. We present an analytical study of the main variance reduction methods used for dealing with this problem. In particular, we discuss the consequences of guiding, replication, and population control on statistical and systematic errors.

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33

BERNARDES, AMERICO T., and DIETRICH STAUFFER. "MONTE CARLO SIMULATION OF AGEING: BEYOND BIT-STRING MODELS." International Journal of Modern Physics C 06, no. 06 (December 1996): 789–806. http://dx.doi.org/10.1142/s0129183195000654.

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Penna's bit-string model of biological ageing due to the accumulation of deleterious mutations is generalized to allow for more than one disease per year. The results remain qualitatively unchanged except for a more complicated non-monotonic approach to equilibrium. We also look at "mutational meltdown", the extinction of the whole population if all mutations are deleterious and heritable, and why the Penna model can escape this extinction. No dependence on population size is found for mutational meltdown, with up to 108 individuals.

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34

Drusano, G. L., S. L. Preston, M. H. Gotfried, L. H. Danziger, and K. A. Rodvold. "Levofloxacin Penetration into Epithelial Lining Fluid as Determined by Population Pharmacokinetic Modeling and Monte Carlo Simulation." Antimicrobial Agents and Chemotherapy 46, no. 2 (February 2002): 586–89. http://dx.doi.org/10.1128/aac.46.2.586-589.2002.

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ABSTRACT Levofloxacin was administered orally to steady state to volunteers randomly in doses of 500 and 750 mg. Plasma and epithelial lining fluid (ELF) samples were obtained at 4, 12, and 24 h after the final dose. All data were comodeled in a population pharmacokinetic analysis employing BigNPEM. Penetration was evaluated from the population mean parameter vector values and from the results of a 1,000-subject Monte Carlo simulation. Evaluation from the population mean values demonstrated a penetration ratio (ELF/plasma) of 1.16. The Monte Carlo simulation provided a measure of dispersion, demonstrating a mean ratio of 3.18, with a median of 1.43 and a 95% confidence interval of 0.14 to 19.1. Population analysis with Monte Carlo simulation provides the best and least-biased estimate of penetration. It also demonstrates clearly that we can expect differences in penetration between patients. This analysis did not deal with inflammation, as it was performed in volunteers. The influence of lung pathology on penetration needs to be examined.

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35

P. Alex, Anu, Manju V.S, and Prinsha T. "Generation of Synthetic Population Using Markov Chain Monte Carlo Simulation Method." International Journal on Cybernetics & Informatics 5, no. 1 (February 28, 2016): 183–91. http://dx.doi.org/10.5121/ijci.2016.5117.

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36

Yang, Mingrui, Elke Pahl, and Joachim Brand. "Improved walker population control for full configuration interaction quantum Monte Carlo." Journal of Chemical Physics 153, no. 17 (November 7, 2020): 174103. http://dx.doi.org/10.1063/5.0023088.

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37

Kimura, Tomoro, and Kazuyuki Nakamura. "Bayesian Estimation of Deer Population Dynamics Using Hamiltonian Monte Carlo Algorithm." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2019 (July 31, 2019): 19–24. http://dx.doi.org/10.5687/sss.2019.19.

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38

Lin, Yulan, Kangtaek Lee, and Themis Matsoukas. "Solution of the population balance equation using constant-number Monte Carlo." Chemical Engineering Science 57, no. 12 (June 2002): 2241–52. http://dx.doi.org/10.1016/s0009-2509(02)00114-8.

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39

Ishida, E. E. O., S. D. P. Vitenti, M. Penna-Lima, J. Cisewski, R. S. de Souza, A. M. M. Trindade, E. Cameron, and V. C. Busti. "cosmoabc: Likelihood-free inference via Population Monte Carlo Approximate Bayesian Computation." Astronomy and Computing 13 (November 2015): 1–11. http://dx.doi.org/10.1016/j.ascom.2015.09.001.

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40

Mingyi Hong, Monica F. Bugallo, and Petar M. Djuric. "Joint Model Selection and Parameter Estimation by Population Monte Carlo Simulation." IEEE Journal of Selected Topics in Signal Processing 4, no. 3 (June 2010): 526–39. http://dx.doi.org/10.1109/jstsp.2010.2048385.

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41

Iacobucci, Alessandra, Jean-Michel Marin, and Christian Robert. "On variance stabilisation in Population Monte Carlo by double Rao-Blackwellisation." Computational Statistics & Data Analysis 54, no. 3 (March 2010): 698–710. http://dx.doi.org/10.1016/j.csda.2008.09.020.

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42

Chikhi, Lounès, Michael W. Bruford, and Mark A. Beaumont. "Estimation of Admixture Proportions: A Likelihood-Based Approach Using Markov Chain Monte Carlo." Genetics 158, no. 3 (July 1, 2001): 1347–62. http://dx.doi.org/10.1093/genetics/158.3.1347.

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Abstract When populations are separated for long periods and then brought into contact for a brief episode in part of their range, this can result in genetic admixture. To analyze this type of event we considered a simple model under which two parental populations (P1 and P2) mix and create a hybrid population (H). After that event, the three populations evolve under pure drift without exchange during T generations. We developed a new method, which allows the simultaneous estimation of the time since the admixture event (scaled by the population size ti = T/Ni, where Ni is the effective population size of population i) and the contribution of one of two parental populations (which we call p1). This method takes into account drift since the admixture event, variation caused by sampling, and uncertainty in the estimation of the ancestral allele frequencies. The method is tested on simulated data sets and then applied to a human data set. We find that (i) for single-locus data, point estimates are poor indicators of the real admixture proportions even when there are many alleles; (ii) biallelic loci provide little information about the admixture proportion and the time since admixture, even for very small amounts of drift, but can be powerful when many loci are used; (iii) the precision of the parameters' estimates increases with sample size (n = 50 vs. n = 200) but this effect is larger for the ti's than for p1; and (iv) the increase in precision provided by multiple loci is quite large, even when there is substantial drift (we found, for instance, that it is preferable to use five loci than one locus, even when drift is 100 times larger for the five loci). Our analysis of a previously studied human data set illustrates that the joint estimation of drift and p1 can provide additional insights into the data.

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43

Anile, A. Marcello, and Simon D. Hern. "Two-valley Hydrodynamical Models for Electron Transport in Gallium Arsenide: Simulation of Gunn Oscillations." VLSI Design 15, no. 4 (January 1, 2002): 681–93. http://dx.doi.org/10.1080/1065514021000012291.

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To accurately describe non-stationary carrier transport in GaAs devices, it is necessary to use Monte Carlo methods or hydrodynamical (or energy transport) models which incorporate population transfer between valleys.We present here simulations of Gunn oscillations in a GaAs diode based on two-valley hydrodynamical models: the classic Bløtekjær model and two recently developed moment expansion models. Scattering parameters within the models are obtained from homogeneous Monte Carlo simulations, and these are compared against expressions in the literature. Comparisons are made between our hydrodynamical results, existing work, and direct Monte Carlo simulations of the oscillator device.

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44

Skenderović, Ivan, Gregor Kotalczyk, and Frank Kruis. "Dual Population Balance Monte Carlo Simulation of Particle Synthesis by Flame Spray Pyrolysis." Processes 6, no. 12 (December 6, 2018): 253. http://dx.doi.org/10.3390/pr6120253.

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The Dual Population Balance Monte Carlo Method (DPBMC) takes into account the full size spectrum of the droplet and particle phase. Droplet and particle size distributions are rendered by weighted simulation particles. This allows for an accurate description of particle nucleation and coagulation and droplet combustion, simultaneously. Internal droplet properties such as temperature and concentrations fields are used to define criteria for the onset of droplet breakage in the framework of weighted Monte Carlo droplets. We discuss the importance of droplet polydispersity on particle formation in metal oxide particle synthesis, which is shown to strongly affect particle formation and growth. The method is applied to particle synthesis from metal nitrate precursor solutions with flame spray pyrolysis (FSP) and compared to experiments from literature.

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45

THOMAS, STUART C., and WILLIAM G. HILL. "Sibship reconstruction in hierarchical population structures using Markov chain Monte Carlo techniques." Genetical Research 79, no. 3 (June 2002): 227–34. http://dx.doi.org/10.1017/s0016672302005669.

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Markov chain Monte Carlo procedures allow the reconstruction of full-sibships using data from genetic marker loci only. In this study, these techniques are extended to allow the reconstruction of nested full- within half-sib families, and to present an efficient method for calculating the likelihood of the observed marker data in a nested family. Simulation is used to examine the properties of the reconstructed sibships, and of estimates of heritability and common environmental variance of quantitative traits obtained from those populations. Accuracy of reconstruction increases with increasing marker information and with increasing size of the nested full-sibships, but decreases with increasing population size. Estimates of variance component are biased, with the direction and magnitude of bias being dependent upon the underlying errors made during pedigree reconstruction.

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46

HUANG, HENG-LIANG, and JING-YANG JOU. "BOOTSTRAP MONTE CARLO WITH ADAPTIVE STRATIFICATION FOR POWER ESTIMATION." Journal of Circuits, Systems and Computers 11, no. 04 (August 2002): 333–50. http://dx.doi.org/10.1142/s0218126602000495.

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Monte Carlo approach for power estimation is based on the assumption that the samples of power are Normally distributed. However, the power distribution of a circuit is not always Normal in the real world. In this paper, the Bootstrap method is adopted to adjust the confidence interval and redeem the deficiency of the conventional Monte Carlo method. Besides, a new input sequence stratification technique for power estimation is proposed. The proposed technique utilizes a multiple regression method to compute the coefficient matrix of the indicator function for stratification. This new stratification technique can adaptively update the coefficient matrix and keep the population of input vectors in a better stratification status. The experimental results demonstrate that the proposed Bootstrap Monte Carlo method with adaptive stratification can effectively reduce the simulation time and meet the user-specified confidence level and error level.

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47

Wang, Wenlong. "An introduction to the Markov chain Monte Carlo method." American Journal of Physics 90, no. 12 (December 2022): 921–34. http://dx.doi.org/10.1119/5.0122488.

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We present an intuitive, conceptual, and semi-rigorous introduction to the Markov Chain Monte Carlo method using a simple model of population dynamics and focusing on a few elementary distributions. We start from two states, then three states, and finally generalize to many states with both discrete and continuous distributions. Despite the mathematical simplicity, our examples include the essential concepts of the Markov Chain Monte Carlo method, including ergodicity, global balance and detailed balance, proposal or selection probability, acceptance probability, the underlying stochastic matrix, and error analysis. Our experience suggests that most senior undergraduate students in physics can follow these materials without much difficulty.

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Parenica, Holly Marie, Christopher Kabat, Pamela Myers, Neil Kirby, Pavlos Papaconstadopoulos, Nikos Papanikolaou, and Sotirios Stathakis. "4089 Clinical Implementation of Monte Carlo Dose Calculation for Patient-Specific Radiotherapy Quality Assurance." Journal of Clinical and Translational Science 4, s1 (June 2020): 106. http://dx.doi.org/10.1017/cts.2020.327.

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OBJECTIVES/GOALS: The Monte Carlo dose calculation method is often considered the “gold standard” for patient dose calculations and can be as radiation dose measurements. Our study aims to develop a true Monte Carlo model that can be implemented in our clinic as part of our routine patient-specific quality assurance. METHODS/STUDY POPULATION: We have configured and validated a model of one of our linear accelerators used for radiation therapy treatments using the EGSnrc Monte Carlo simulation software. Measured dosimetric data was obtained from the linear accelerator and was used as the standard to compare the doses calculated with our model in EGSnrc. We will compare dose calculations between commercial treatment planning systems, the EGSnrc Monte Carlo model, and patient-specific measurements. We will implement the Monte Carlo model in our clinic for routine second-checks of patient plans, and to recalculate plans delivered to patients using machine log files. RESULTS/ANTICIPATED RESULTS: Our Monte Carlo model is within 1% agreement with our measured dosimetric data, and is an accurate representation of our linear accelerators used for patient treatments. With this high level of accuracy, we have begun simulating more complex patient treatment geometries, and expect the level of accuracy to be within 1% of measured data. We believe the Monte Carlo calculation based on machine log files will correlate with patient-specific QA analysis and results. The Monte Carlo model will be a useful tool in improving our patient-specific quality assurance protocol and can be utilized in further research. DISCUSSION/SIGNIFICANCE OF IMPACT: This work can be implemented directly in clinical practice to ensure patient doses are calculated as accurately as possible. These methods can be used by clinics who do not have access to more advanced dose calculation software, ensuring accuracy for all patients undergoing radiotherapy treatments.

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Camacho, J., S. Torres, and E. García-Berro. "Monte Carlo simulations of the binary white dwarf population: A progress report." Journal of Physics: Conference Series 172 (June 1, 2009): 012030. http://dx.doi.org/10.1088/1742-6596/172/1/012030.

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Webb, Russell Y., and Peter J. Smith. "A POPULATION MONTE CARLO METHOD FOR GENERATING RANDOM MATRICES WITH KNOWN CHARACTERISTICS." Applied Artificial Intelligence 22, no. 7-8 (August 26, 2008): 730–48. http://dx.doi.org/10.1080/08839510802164143.

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