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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 and 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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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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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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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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An MCMC Method to Sample from Lattice Distributions [PDF]
2021 IEEE International Symposium on Information Theory (ISIT), 2021 11 pages, 7 ...
Anand Jerry George, Navin Kashyap
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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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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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