Bayesian parameter inference by Markov chain Monte Carlo with hybrid fitness measures: theory and test in apoptosis signal transduction network. [PDF]
PLoS ONE, 2013 When model parameters in systems biology are not available from experiments, they need to be inferred so that the resulting simulation reproduces the experimentally known phenomena. For the purpose, Bayesian statistics with Markov chain Monte Carlo (MCMC)
Yohei Murakami, Shoji Takada
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Laplace approximation for conditional autoregressive models for spatial data of diseases
MethodsX, 2022 Conditional autoregressive (CAR) distributions are used to account for spatial autocorrelation in small areal or lattice data to assess the spatial risks of diseases.
Guiming Wang
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Bayesian inference on reliability parameter with non-identical-component strengths for Rayleigh distribution [PDF]
Journal of Mahani Mathematical ResearchIn this paper, we delve into Bayesian inference related to multi-component stress-strength parameters, focusing on non-identical component strengths within a two-parameter Rayleigh distribution under the progressive first failure censoring scheme.
Akram Kohansal
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Subsampling MCMC - An introduction for the survey statistician [PDF]
, 2018 The rapid development of computing power and efficient Markov Chain Monte Carlo (MCMC) simulation algorithms have revolutionized Bayesian statistics, making it a highly practical inference method in applied work.
Dang, Khue-Dung +4 more
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Heliyon, 2023 In this extant paper, a multivariate time series model using the seemingly unrelated times series equation (SUTSE) framework is proposed to forecast the peak and short-term electricity demand using time series data from February 2, 2014, to August 2 ...
Frank Kofi Owusu +6 more
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Estimating the Volume of the Solution Space of SMT(LIA) Constraints by a Flat Histogram Method
Algorithms, 2018 The satisfiability modulo theories (SMT) problem is to decide the satisfiability of a logical formula with respect to a given background theory. This work studies the counting version of SMT with respect to linear integer arithmetic (LIA), termed SMT(LIA)
Wei Gao +3 more
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Iran's Exchange Market in Five Episodes: Bayesian Estimation of Systematic Risk with MCMC Method [PDF]
Mathematics and Modeling in FinanceThis paper estimates systematic risk in Iran’s foreign exchange market using a stochastic volatility model, analyzing five distinct episodes shaped by varying economic and political conditions. By tracing the evolution of volatility dynamics across these
Amir Mohsen Moradi +2 more
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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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A fast algorithm for BayesB type of prediction of genome-wide estimates of genetic value
Genetics Selection Evolution, 2009 Genomic selection uses genome-wide dense SNP marker genotyping for the prediction of genetic values, and consists of two steps: (1) estimation of SNP effects, and (2) prediction of genetic value based on SNP genotypes and estimates of their effects.
Shepherd Ross +3 more
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Preconditioning Markov Chain Monte Carlo Simulations Using Coarse-Scale Models [PDF]
, 2006 We study the preconditioning of Markov chain Monte Carlo (MCMC) methods using coarse-scale models with applications to subsurface characterization. The purpose of preconditioning is to reduce the fine-scale computational cost and increase the acceptance ...
Efendiev, Y., Hou, T., Luo, W.
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