Results 21 to 30 of about 38,389 (265)
MCMC and GLMs for estimating regression parameters: Evidence from non-life Egyptian insurance sector [PDF]
Journal of Humanities and Applied Social Sciences, 2019 Purpose – The purpose of this study is to estimate the linear regression parameters using two alternative techniques. First technique is to apply the generalized linear model (GLM) and the second technique is the Markov Chain Monte Carlo (MCMC) method ...
Mahmoud ELsayed, Amr Soliman
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Sticky proposal densities for adaptive MCMC methods [PDF]
2016 IEEE Statistical Signal Processing Workshop (SSP), 2016 Monte Carlo (MC) methods are commonly used in Bayesian signal processing to address complex inference problems. The performance of any MC scheme depends on the similarity between the proposal (chosen by the user) and the target (which depends on the problem).
Luca Martino +2 more
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EM algorithm for Bayesian estimation of genomic breeding values
BMC Genetics, 2010 Background In genomic selection, a model for prediction of genome-wide breeding value (GBV) is constructed by estimating a large number of SNP effects that are included in a model.
Iwata Hiroyoshi, Hayashi Takeshi
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Agriculture, 2020 Particle filter has received increasing attention in data assimilation for estimating model states and parameters in cases of non-linear and non-Gaussian dynamic processes.
Alaa Jamal, Raphael Linker
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Efficient Bayesian Inverse Modeling of Water Infiltration in Layered Soils
Vadose Zone Journal, 2019 Modeling water movement in heterogeneous soils, e.g., layered soils, is an essential but challenging task that requires accurate estimation of multiple sets of soil hydraulic parameters.
Hongbei Gao +6 more
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Orthogonal parallel MCMC methods for sampling and optimization [PDF]
Digital Signal Processing, 2016 Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In order to foster better exploration of the state space, specially in high-dimensional applications, several schemes employing multiple ...
Luca Martino +4 more
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Limit theorems for sequential MCMC methods [PDF]
Advances in Applied Probability, 2020 AbstractBoth sequential Monte Carlo (SMC) methods (a.k.a. ‘particle filters’) and sequential Markov chain Monte Carlo (sequential MCMC) methods constitute classes of algorithms which can be used to approximate expectations with respect to (a sequence of) probability distributions and their normalising constants.
Finke, A, Doucet, A, Johansen, AM
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Longitudinal Data Analysis Based on Bayesian Semiparametric Method
Axioms, 2023 A Bayesian semiparametric model framework is proposed to analyze multivariate longitudinal data. The new framework leads to simple explicit posterior distributions of model parameters.
Guimei Jiao +8 more
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Научно-технический вестник информационных технологий, механики и оптикиIn biology, information about interactions between the proteins or genes under study can be represented as a biological graph. A connected subgraph, whose vertices perform a common biological function, is called an active module.
D. A. Usoltsev +4 more
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Open Research Europe, 2023 Phylogenetic estimation is, and has always been, a complex endeavor. Estimating a phylogenetic tree involves evaluating many possible solutions and possible evolutionary histories that could explain a set of observed data, typically by using a model of ...
Orlando Schwery +4 more
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