Results 11 to 20 of about 132,359 (288)
Alternatives to the MCMC method [PDF]
AIP Conference Proceedings, 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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label.switching: An R Package for Dealing with the Label Switching Problem in MCMC Outputs [PDF]
Journal of Statistical Software, 2016 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
Panagiotis Papastamoulis
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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
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Statistica, 2021 Based on the hybrid censored samples, this article deals with the problem of point and interval estimation of the stress-strength reliability R = P(Y < X) when X and Y both have independent generalized inverted exponential distributions with different ...
Renu Garg, Kapil Kumar
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Bayesian back analysis considering constraints
Yantu gongcheng xuebao, 2021 Soil parameters significantly affect the prediction performance of geotechnical models. In the field of parameter identification, the MCMC-based Bayesian method is an effective way to infer the probability distribution of soil parameters.
TAO Yuan-qin 1 , SUN Hong-lei 2, CAI Yuan-qiang 1, 2
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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
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Statistica, 2021 In this paper we deal with the modelling of cumulative incidence function using improper Gompertz distribution based on middle censored competing risks survival data. Together with the unknown parameters, cumulative incidence function also estimated.
Habbiburr Rehman, Navin Chandra
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Parameter estimation for X-ray scattering analysis with Hamiltonian Markov Chain Monte Carlo
Journal of Synchrotron Radiation, 2022 Bayesian-inference-based approaches, in particular the random-walk Markov Chain Monte Carlo (MCMC) method, have received much attention recently for X-ray scattering analysis.
Zhang Jiang +4 more
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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
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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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