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pro vyhledávání: '"Sutton, Matthew"'
Vertical federated learning (VFL) has emerged as a paradigm for collaborative model estimation across multiple clients, each holding a distinct set of covariates. This paper introduces the first comprehensive framework for fitting Bayesian models in
Externí odkaz:
http://arxiv.org/abs/2405.04043
Autor:
South, Leah, Sutton, Matthew
This chapter describes several control variate methods for improving estimates of expectations from MCMC.
Comment: To appear in the second edition of the Handbook of MCMC. Associated code available at https://github.com/LeahPrice/CVBookChapter/
Comment: To appear in the second edition of the Handbook of MCMC. Associated code available at https://github.com/LeahPrice/CVBookChapter/
Externí odkaz:
http://arxiv.org/abs/2402.07349
Monte Carlo methods - such as Markov chain Monte Carlo (MCMC) and piecewise deterministic Markov process (PDMP) samplers - provide asymptotically exact estimators of expectations under a target distribution. There is growing interest in alternatives
Externí odkaz:
http://arxiv.org/abs/2306.15422
Reversible jump Markov chain Monte Carlo (RJMCMC) proposals that achieve reasonable acceptance rates and mixing are notoriously difficult to design in most applications. Inspired by recent advances in deep neural network-based normalizing flows and d
Externí odkaz:
http://arxiv.org/abs/2210.12572
New sampling algorithms based on simulating continuous-time stochastic processes called piece-wise deterministic Markov processes (PDMPs) have shown considerable promise. However, these methods can struggle to sample from multi-modal or heavy-tailed
Externí odkaz:
http://arxiv.org/abs/2205.09559
Autor:
Sutton, Matthew, Fearnhead, Paul
Recently non-reversible samplers based on simulating piecewise deterministic Markov processes (PDMPs) have shown potential for efficient sampling in Bayesian inference problems. However, there remains a lack of guidance on how to best implement these
Externí odkaz:
http://arxiv.org/abs/2112.12897
Detection of induced polarisation (IP) effects in airborne electromagnetic (AEM) measurements does not yet have an established methodology. This contribution develops a Bayesian approach to the IP-detectability problem using decoupled transdimensiona
Externí odkaz:
http://arxiv.org/abs/2109.00661
A new class of Markov chain Monte Carlo (MCMC) algorithms, based on simulating piecewise deterministic Markov processes (PDMPs), have recently shown great promise: they are non-reversible, can mix better than standard MCMC algorithms, and can use sub
Externí odkaz:
http://arxiv.org/abs/2010.11771
Publikováno v:
In Social Science & Medicine October 2023 334