Results 11 to 20 of about 29,218,928 (220)
Alternatives To The MCMC Method [PDF]
AIP Conference Proceedings, 2004 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. With the MCMC method it is not necessary to compute the normalising constant (see e.g. Tierney, 1994; Besag, 2000).
L. Knüsel
semanticscholar +3 more sources
Estimation of Hyperbolic Diffusion Using MCMC Method [PDF]
, 2002 In this paper we propose a Bayesian method for estimating hyperbolic diffusion models. The approach is based on the Markov Chain Monte Carlo (MCMC) method after discretization via the Milstein scheme.
Jun Yu, Xibin Zhang, Y.K. Tse
core +2 more sources
On the Markov Chain Monte Carlo (MCMC) method
Sadhana, 2006 Let \(f(x)\) be a density of a distribution of some random variable \(X.\) We are interested in computing the integral \(\int\limits g(x) f(x)\,dx = E g(X)\) for a given function \(g.\) If we can generate a random sample \(x_1, \ldots, x_n\) of size \(n\) from this distribution and compute \(a_n ={1\over n} \sum_{i=1}^n g(x_i)\), then by the law of ...
R. Karandikar
semanticscholar +4 more sources
MCMC METHODS FOR DIFFUSION BRIDGES [PDF]
Stochastics and Dynamics, 2008 We present and study a Langevin MCMC approach for sampling nonlinear diffusion bridges. The method is based on recent theory concerning stochastic partial differential equations (SPDEs) reversible with respect to the target bridge, derived by applying the Langevin idea on the bridge pathspace.
Beskos, Alexandros +3 more
openaire +3 more sources
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
openaire +4 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.
core +2 more sources
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
doaj +1 more source
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
doaj +1 more source
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
core +1 more source
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
doaj +1 more source