Results 31 to 40 of about 168,901 (276)
MCMC genome rearrangement [PDF]
Bioinformatics, 2003 Abstract Motivation: As more and more genomes have been sequenced, genomic data is rapidly accumulating. Genome-wide mutations are believed more neutral than local mutations such as substitutions, insertions and deletions, therefore phylogenetic investigations based on inversions, transpositions and inverted transpositions are less ...
openaire +2 more sources
Alternatives to the MCMC method [PDF]
, 2003 The Markov Chain Monte Carlo method (MCMC) is often used to generate independent (pseudo) random numbers from a distribution with a density that is known only up to a normalising constant.
Knüsel, L.
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Spbsampling: An R Package for Spatially Balanced Sampling
Journal of Statistical Software, 2022 The basic idea underpinning the theory of spatially balanced sampling is that units closer to each other provide less information about a target of inference than units farther apart.
Francesco Pantalone +2 more
doaj +1 more source
label.switching: An R Package for Dealing with the Label Switching Problem in MCMC Outputs [PDF]
, 2015 Label switching is a well-known and fundamental problem in Bayesian estimation of mixture or hidden Markov models. In case that the prior distribution of the model parameters is the same for all states, then both the likelihood and posterior distribution
Papastamoulis, Panagiotis
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pexm: A JAGS Module for Applications Involving the Piecewise Exponential Distribution
Journal of Statistical Software, 2021 In this study, we present a new module built for users interested in a programming language similar to BUGS to fit a Bayesian model based on the piecewise exponential (PE) distribution.
Vinícius D. Mayrink +2 more
doaj +1 more source
MCMC-ODPR : primer design optimization using Markov Chain Monte Carlo sampling [PDF]
, 2012 Background Next generation sequencing technologies often require numerous primer designs that require good target coverage that can be financially costly.
Allaby, Robin G. +3 more
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TI-Stan: Adaptively Annealed Thermodynamic Integration with HMC †
Proceedings, 2019 We present a novel implementation of the adaptively annealed thermodynamic integration technique using Hamiltonian Monte Carlo (HMC). Thermodynamic integration with importance sampling and adaptive annealing is an especially useful method for estimating ...
R. Wesley Henderson, Paul M. Goggans
doaj +1 more source
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
doaj +1 more source
Open Physics, 2017 The coefficient of variation [CV] has several applications in applied statistics. So in this paper, we adopt Bayesian and non-Bayesian approaches for the estimation of CV under type-II censored data from extension exponential distribution [EED].
Bakoban Rana A.
doaj +1 more source
Orthogonal parallel MCMC methods for sampling and optimization
, 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
Corander, J. +4 more
core +2 more sources