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An adaptive Hessian approximated stochastic gradient MCMC method [PDF]
Journal of Computational Physics, 2021 Bayesian approaches have been successfully integrated into training deep neural networks. One popular family is stochastic gradient Markov chain Monte Carlo methods (SG-MCMC), which have gained increasing interest due to their scalability to handle large datasets and the ability to avoid overfitting.
Yating Wang +2 more
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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).
Knüsel, L., Knüsel, Leo
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The shifted ODE method for underdamped Langevin MCMC
CoRR, 2021 In this paper, we consider the underdamped Langevin diffusion (ULD) and propose a numerical approximation using its associated ordinary differential equation (ODE). When used as a Markov Chain Monte Carlo (MCMC) algorithm, we show that the ODE approximation achieves a $2$-Wasserstein error of $\varepsilon$ in $\mathcal{O}\big(d^{\frac{1}{3 ...
James Foster +2 more
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Scaling up Bayesian population phylogenomics through virtual dimension reduction [PDF]
Nature CommunicationsPopulation phylogenomics uses sampled genomes to jointly infer population genetic processes (ancestral and contemporary population sizes, historical gene flow) and a phylogenetic tree relating species or populations including species split times.
Tomáš Flouri +4 more
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A stable manifold MCMC method for high dimensions [PDF]
Statistics & Probability Letters, 2014 We combine two important recent advancements of MCMC algorithms: first, methods utilizing the intrinsic manifold structure of the parameter space; then, algorithms effective for targets in infinite-dimensions with the critical property that their mixing time is robust to mesh refinement.
Alexandros Beskos
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On the Markov Chain Monte Carlo (MCMC) method [PDF]
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 ...
Karandikar, Rajeeva L.
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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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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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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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