Information-Geometric Markov Chain Monte Carlo Methods Using Diffusions [PDF]
, 2014 Recent work incorporating geometric ideas in Markov chain Monte Carlo is reviewed in order to highlight these advances and their possible application in a range of domains beyond statistics. A full exposition of Markov chains and their use in Monte Carlo
Livingstone, Samuel +5 more
core +1 more source
Markov chain Monte Carlo methods for state-space models with point process observations [PDF]
, 2012 This letter considers how a number of modern Markov chain Monte Carlo (MCMC) methods can be applied for parameter estimation and inference in state-space models with point process observations.
Niranjan, Mahesan +2 more
core +1 more source
Statistical Inference on Simple Step-Stress Accelerated Life Testing for Gompertz Distribution Under .Progressive Type-II Censoring [PDF]
Maǧallaẗ Al-Buḥūṯ Al-TiǧāriyyaẗWe consider a simple step-stress model under the Gompertz distribution (GD) when the available data are type-II progressive censored. The cumulative exposure model is assumed when the lifetime of test units follows a Gompertz distribution.
السيد وليد شعبان عبدالمنتصر +2 more
doaj +1 more source
A comparison of approximate versus exact techniques for Bayesian parameter inference in nonlinear ordinary differential equation models [PDF]
Royal Society Open Science, 2020 The behaviour of many processes in science and engineering can be accurately described by dynamical system models consisting of a set of ordinary differential equations (ODEs).
Amani A. Alahmadi +4 more
doaj +1 more source
Application of Markov chain Monte carlo method in Bayesian statistics
MATEC Web of Conferences, 2016 In statistical inference methods, bayesian method is a method of great influence. This paper introduces the basic idea of the bayesian method. However, the widespread popularity of MCMC samplers is largely due to their impact on solving statistical ...
Zhao Qi
doaj +1 more source
Variational Hybrid Monte Carlo for Efficient Multi-Modal Data Sampling
Entropy, 2023 The Hamiltonian Monte Carlo (HMC) sampling algorithm exploits Hamiltonian dynamics to construct efficient Markov Chain Monte Carlo (MCMC), which has become increasingly popular in machine learning and statistics.
Shiliang Sun +3 more
doaj +1 more source
Neural Langevin Dynamical Sampling
IEEE Access, 2020 Sampling technique is one of the asymptotically unbiased estimation approaches for inference in Bayesian probabilistic models. Markov chain Monte Carlo (MCMC) is a kind of sampling methods, which is widely used in the inference of complex probabilistic ...
Minghao Gu, Shiliang Sun
doaj +1 more source
Planning Tunnel Construction Using Markov Chain Monte Carlo (MCMC) [PDF]
Mathematical Problems in Engineering, 2015 Tunnels, drifts, drives, and other types of underground excavation are very common in mining as well as in the construction of roads, railways, dams, and other civil engineering projects. Planning is essential to the success of tunnel excavation, and construction time is one of the most important factors to be taken into account.
Juan P. Vargas +3 more
openaire +1 more source
ESTIMATION FOR THE WEIBULL-GEOMETRIC DISTRIBUTION BASED ON CONSTANT PARTIALLY ACCELERATED LIFE TESTS VIA MCMC TECHNIQUE [PDF]
Assiut University Journal of Multidisciplinary Scientific Research, 2018 In this article, constant partially accelerated life tests are considered. Based on a progressive first-failure censoring scheme, the maximum likelihood and the Bayes estimates for the parameters of the Weibull-Geometric distribution as well as the ...
doaj +1 more source
Geodesic Monte Carlo on Embedded Manifolds [PDF]
, 2013 Markov chain Monte Carlo methods explicitly defined on the manifold of probability distributions have recently been established. These methods are constructed from diffusions across the manifold and the solution of the equations describing geodesic flows
Simon Byrne +5 more
core +1 more source