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Relevant bibliographies by topics / Population Monte Carlo

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Academic literature on the topic 'Population Monte Carlo'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 2023

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Journal articles on the topic "Population Monte Carlo"

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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Population Monte Carlo"

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Bakra, Eleni. "Aspects of population Markov chain Monte Carlo and reversible jump Markov chain Monte Carlo." Thesis, University of Glasgow, 2009. http://theses.gla.ac.uk/1247/.

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Anderson, Eric C. "Monte Carlo methods for inference in population genetic models /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/6368.

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Rousset, Mathias. "Méthodes de "Population Monte-Carlo'' en temps continu est physique numérique." Toulouse 3, 2006. http://www.theses.fr/2006TOU30251.

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Dans cette thèse, nous nous intéressons aux méthodes numériques probabilistes dites de Population Monte-Carlo, du point de vue du temps continu. Ces méthodes PMC se ramènent au calcul séquentiel de moyennes pondérées de trajectoires Markoviennes. Nous démontrons la convergence (vers la fonction propre principale des opérateurs de Schrödinger) en temps long de la variance et du biais de cette méthode avec la bonne vitesse en 1/N. Ensuite, nous considérons le problème de l'échantillonnage séquentiel d'un flot continu de mesures de Boltzmann. Pour cela, à partir d'une dynamique Markovienne arbitraire, nous associons une dynamique renversée dans le temps dont la loi pondérée par une moyenne trajectorielle de Feynman-Kac explicitement calculable redonne la dynamique initiale ainsi que la mesure de Boltzmann à calculer. Enfin, nous généralisons ce problème au cas où la dynamique est due à l'évolution dans le temps de contraintes rigides sur les configurations possibles du processus. Nous calculons exactement les poids associés, qui font intervenir la courbure locale des sous-variétés générées par les contraintes.
In this dissertation, we focus on stochastic numerical methods of Population Monte-Carlo type, in the continuous time setting. These PMC methods resort to the sequential computation of averages of weighted Markovian paths. The practical implementation rely then on the time evolution of the empirical distribution of a system of N interacting walkers. We prove the long time convergence (towards Schrödinger groundstates) of the variance and bias of this method with the expected 1/N rate. Next, we consider the problem of sequential sampling of a continuous flow of Boltzmann measures. For this purpose, starting with any Markovian dynamics, we associate a second dynamics in reversed time whose law (weighted by a computable Feynman-Kac path average) gives out the original dynamics as well as the target Boltzmann measure. Finally, we generalize the latter problem to the case where the dynamics is caused by evolving rigid constraints on the positions of the process. We compute exactly the associated weights, which resorts to the local curvature of the manifold defined by the constraints

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Ding, Jie. "Monte Carlo Pedigree Disequilibrium Test with Missing Data and Population Structure." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1218475579.

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Fan, Gailing. "Galaxy radio pulsar population modelling and magellanic clouds radio pulsar survey /." Hong Kong : University of Hong Kong, 2002. http://sunzi.lib.hku.hk/hkuto/record.jsp?B25059294.

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Lunn, David Jonathan. "The application of Markov chain Monte Carlo techniques to the study of population pharmacokinetics." Thesis, University of Manchester, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.488145.

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Camacho, Díaz Judit. "Monte Carlo simulations of the population of single and binary white dwarfs of our galaxy." Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/145924.

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Since white dwarfs are the final stage of the evolution of the vast majority of stars, they carry important information about the chemical evolution of our Galaxy, its star formation rate, and its structure and dynamics. This thesis pays attention to two related but distinct astrophysical problems involving white dwarfs. The first of these problems concern the nature and the location of the microlensing events towards the Large Magellanic Clouds (LMC), which still remains a mystery. The main observational groups, MACHO and EROS, are in dispute each, yet agreement has now been reached in some of the most important points. The second of the problems we address in this thesis is an open problem as well. Close compact binaries are at the heart of several interesting phenomena in our Galaxy as well. Close compact binaries are formed through at least one common envelope episode. Even though the basics concepts of the evolution during a common envelope phase are rather simple, the details are still far from being well understood. To shed light on these problems, we used an existing Monte Carlo simulator. We expanded this simulator integrating the most up-to-date white dwarfs cooling models as well as detailed modeling of our Galaxy and the LMC in order to mimic both the MACHO and EROS experiments. Additionally, we included the red dwarf population and performed a joint analysis of the contributions of both populations to the dark matter content of our Galaxy. Moreover, we studied the contribution of the subpopulation of white dwarfs with hydrogen-deficient atmospheres. On the other hand, our Monte Carlo code has been expanded to deal with those systems composed by a white dwarf and a main sequence star, which have evolved through a common episode. A detailed implementation of several different physical processes, including a full description of the mass transfer episode, a complete treatment of the Roche lobe overflow episode, gravitational tides and orbital evolution of the binary system, was performed. Furthermore, in our treatment we carefully included all the different selection criteria and observational biases. This allowed us to make a meaningful comparison with the available data, besides examining the role played by the binding energy parameter and by the common envelope parameter, not to mention the role played by the distribution of secondary masses of the binary systems. The results of our Monte Carlo simulations of the microlensing experiments show agreement with the findings of the EROS and MACHO survey. Our findings show that neither white dwarfs nor red dwarfs can be major contributors to the microlensing depth towards the LMC. These facts reinforce the idea, previously pointed by others studies, that the optical depth found by the MACHO survey is highly likely an overestimate, probably due to contamination of self-lensing objects, amid other possible explanations. Concerning the second point of this thesis, our Monte Carlo simulations correctly reproduce the properties of the observed population of post-common envelope white dwarf plus main sequence binaries, once biases are taken into account. The best-fit models are obtained with fractions less than ~20% of the internal energy contributing to the ejection of the star progenitor¿s envelope, and values for the common-envelope efficiency parameter less than ~0.3. To conclude, the work presented in this thesis poses an important step forward not only in constraining the microlensing discoveries, but also in validating models for the observed white-dwarf populations of our Galaxy.

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范改玲 and Gailing Fan. "Galaxy radio pulsar population modelling and magellanic clouds radio pulsar survey." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B31243058.

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Louw, Markus. "A population Monte Carlo approach to estimating parametric bidirectional reflectance distribution functions through Markov random field parameter estimation." Doctoral thesis, University of Cape Town, 2009. http://hdl.handle.net/11427/5179.

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In this thesis, we propose a method for estimating the parameters of a parametric bidirectional reflectance distribution function (BRDF) for an object surface. The method uses a novel Markov Random Field (MRF) formulation on triplets of corner vertex nodes to model the probability of sets of reflectance parameters for arbitrary reflectance models, given probabilistic surface geometry, camera, illumination, and reflectance image information. In this way, the BRDF parameter estimation problem is cast as a MRF parameter estimation problem. We also present a novel method for estimating the MRF parameters, which uses Population Monte Carlo (PMC) sampling to yield a posterior distribution over the parameters of the BRDF. This PMC based method for estimating the posterior distribution on MRF parameters is compared, using synthetic data, to other parameter estimation methods based on Markov Chain Monte Carlo (MCMC) and Levenberg-Marquardt nonlinear minimization, where it is found to have better results for convergence to the known correct synthetic data parameter sets than the MCMC based methods, and similar convergence results to the LM method. The posterior distributions on the parametric BRDFs for real surfaces, which are represented as evolved sample sets calculated using a Population Monte Carlo algorithm, can be used as features in other high-level vision material or surface classification methods. A variety of probabilistic distances between these features, including the Kullback-Leibler divergence, the Bhattacharyya distance and the Patrick-Fisher distance is used to test the classifiability of the materials, using the PMC evolved sample sets as features. In our experiments on real data, which comprises 48 material surfaces belonging to 12 classes of material, classification errors are counted by comparing the 1-nearest-neighbour classification results to the known (manually specified) material classes. Other classification error statistics such as WNN (worst nearest neighbour) are also calculated. The symmetric Kullback-Leibler divergence, used as a distance measure between the PMC developed sample sets, is the distance measure which gives the best classification results on the real data, when using the 1-nearest neighbour classification method. It is also found that the sets of samples representing the posterior distributions over the MRF parameter spaces are better features for material surface classification than the optimal MRF parameters returned by multiple-seed Levenberg-Marquardt minimization algorithms, which are configured to find the same MRF parameters. The classifiability of the materials is also better when using the entire evolved sample sets (calculated by PMC) as classification features than it is when using only the maximum a-posteriori sample from the PMC evolved sample sets as the feature for each material. It is therefore possible to calculate usable parametric BRDF features for surface classification, using our method.

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Li, Qianqiu. "Bayesian inference on dynamics of individual and population hepatotoxicity via state space models." Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1124297874.

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Thesis (Ph. D.)--Ohio State University, 2005.
Title from first page of PDF file. Document formatted into pages; contains xiv, 155 p.; also includes graphics (some col.). Includes bibliographical references (p. 147-155). Available online via OhioLINK's ETD Center

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Books on the topic "Population Monte Carlo"

1

Anderson, Gordon. Nonparametric tests for common but unspecified population distributions: A Monte Carlo comparison. Toronto: Dept. of Economics and Institute for Policy Analysis, University of Toronto, 1994.

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2

Lunn, David Jonathan. The application of Markov chain Monte Carlo techniques to the study of population pharmacokinetics. Manchester: University of Manchester, 1995.

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3

Levin, Ines, and Betsy Sinclair. Causal Inference with Complex Survey Designs. Edited by Lonna Rae Atkeson and R. Michael Alvarez. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190213299.013.4.

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This article discusses methods that combine survey weighting and propensity score matching to estimate population average treatment effects. Beginning with an overview of causal inference techniques that incorporate data from complex surveys and the usefulness of survey weights, it then considers approaches for incorporating survey weights into three matching algorithms, along with their respective methodologies: nearest-neighbor matching, subclassification matching, and propensity score weighting. It also presents the results of a Monte Carlo simulation study that illustrates the benefits of incorporating survey weights into propensity score matching procedures, as well as the problems that arise when survey weights are ignored. Finally, it explores the differences between population-based inferences and sample-based inferences using real-world data from the 2012 panel of The American Panel Survey (TAPS). The article highlights the impact of social media usage on political participation, when such impact is not actually apparent in the target population.

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Book chapters on the topic "Population Monte Carlo"

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Liu, Jun S. "Population-Based Monte Carlo Methods." In Springer Series in Statistics, 225–43. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-0-387-76371-2_11.

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Kotalczyk, Gregor, and Frank Einar Kruis. "Compartmental Population Balances by Means of Monte Carlo Methods." In Dynamic Flowsheet Simulation of Solids Processes, 519–48. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45168-4_15.

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Krogel, Jaron T., and David M. Ceperley. "Population Control Bias with Applications to Parallel Diffusion Monte Carlo." In ACS Symposium Series, 13–26. Washington, DC: American Chemical Society, 2012. http://dx.doi.org/10.1021/bk-2012-1094.ch002.

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Wood, Matt A. "Monte Carlo Simulations of the White Dwarf Population and Luminosity Function." In White Dwarfs, 105–11. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5542-7_17.

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Gimenez, Olivier, Simon J. Bonner, Ruth King, Richard A. Parker, Stephen P. Brooks, Lara E. Jamieson, Vladimir Grosbois, Byron J. T. Morgan, and Len Thomas. "WinBUGS for Population Ecologists: Bayesian Modeling Using Markov Chain Monte Carlo Methods." In Modeling Demographic Processes In Marked Populations, 883–915. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-78151-8_41.

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Nagata, Hiroyasu, Kei-ichi Tainaka, Nariyuki Nakagiri, and Jin Yoshimura. "Monte Carlo Simulation in Lattice Ecosystem: Top-Predator Conservation and Population Uncertainty." In Natural Computing, 145–54. Tokyo: Springer Japan, 2009. http://dx.doi.org/10.1007/978-4-431-88981-6_13.

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Sherri, M., I. Boulkaibet, T. Marwala, and M. I. Friswell. "Bayesian Finite Element Model Updating Using a Population Markov Chain Monte Carlo Algorithm." In Special Topics in Structural Dynamics & Experimental Techniques, Volume 5, 259–69. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47709-7_24.

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Zia, R. K. P., and R. J. Astalos. "Statistics of an Age Structured Population with Two Competing Species: Analytic and Monte Carlo Studies." In Springer Proceedings in Physics, 235–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-59406-9_30.

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Klinger, Emmanuel, and Jan Hasenauer. "A Scheme for Adaptive Selection of Population Sizes in Approximate Bayesian Computation - Sequential Monte Carlo." In Computational Methods in Systems Biology, 128–44. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67471-1_8.

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Graziani, Rebecca. "Stochastic Population Forecasting: A Bayesian Approach Based on Evaluation by Experts." In Developments in Demographic Forecasting, 21–42. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42472-5_2.

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Abstract We suggest a procedure for deriving expert based stochastic population forecasts within the Bayesian approach. According to the traditional and commonly used cohort-component model, the inputs of the forecasting procedures are the fertility and mortality age schedules along with the distribution of migrants by age. Age schedules and distributions are derived from summary indicators, such as total fertility rates, male and female life expectancy at birth, and male and female number of immigrants and emigrants. The joint distributions of all summary indicators are obtained based on evaluations by experts, elicited according to a conditional procedure that makes it possible to derive information on the centres of the indicators, their variability, their across-time correlations, and the correlations between the indicators. The forecasting method is based on a mixture model within the Supra-Bayesian approach that treats the evaluations by experts as data and the summary indicators as parameters. The derived posterior distributions are used as forecast distributions of the summary indicators of interest. A Markov Chain Monte Carlo algorithm is designed to approximate such posterior distributions.

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Conference papers on the topic "Population Monte Carlo"

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Bugallo, Monica F., Mingyi Hong, and Petar M. Djuric. "Marginalized population Monte Carlo." In ICASSP 2009 - 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2009. http://dx.doi.org/10.1109/icassp.2009.4960236.

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El-Laham, Yousef, Petar M. Djuric, and Monica F. Bugallo. "Enhanced Mixture Population Monte Carlo Via Stochastic Optimization and Markov Chain Monte Carlo Sampling." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053410.

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Sweezy, Jeremy, Steve Nolen, Terry Adams, and Anthony Zukaitis. "A Particle Population Control Method for Dynamic Monte Carlo." In SNA + MC 2013 - Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo, edited by D. Caruge, C. Calvin, C. M. Diop, F. Malvagi, and J. C. Trama. Les Ulis, France: EDP Sciences, 2014. http://dx.doi.org/10.1051/snamc/201403202.

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Elvira, Victor, Luca Martino, David Luengo, and Monica F. Bugallo. "Population Monte Carlo schemes with reduced path degeneracy." In 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2017. http://dx.doi.org/10.1109/camsap.2017.8313090.

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Camacho, Judit, Santiago Torres, and Enrique García-Berro. "Monte Carlo simulations of the Galactic binary population." In Supernovae: lights in the darkness. Trieste, Italy: Sissa Medialab, 2008. http://dx.doi.org/10.22323/1.060.0008.

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Hua, Fei, Xiao-hong Shen, Zhao Chen, Fu-zhou Yang, and Jiang-jian Gu. "Bayesian DOA estimation method using Population Monte Carlo." In 2012 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). IEEE, 2012. http://dx.doi.org/10.1109/icspcc.2012.6335671.

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Chen, Xi, and Enlu Zhou. "Population model-based optimization with sequential Monte Carlo." In 2013 Winter Simulation Conference - (WSC 2013). IEEE, 2013. http://dx.doi.org/10.1109/wsc.2013.6721490.

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Wang, Xiangrong. "Segmentation Using Population based Markov Chain Monte Carlo." In 2013 9th International Conference on Natural Computation (ICNC). IEEE, 2013. http://dx.doi.org/10.1109/icnc.2013.6817967.

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Zhu, Dandan, and Kai Jiang. "Population Forecasting Model Based on Monte Carlo Algorithm." In ICCDE 2018: 2018 International Conference on Computing and Data Engineering. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3219788.3219795.

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Chi, Hongmei, and Peter Beerli. "Poster: Quasi-Monte Carlo method in population genetics parameter estimation." In 2011 IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). IEEE, 2011. http://dx.doi.org/10.1109/iccabs.2011.5729891.

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