Zobrazeno 1 - 10
of 763
pro vyhledávání: '"Fearnhead, P"'
This book aims to provide a graduate-level introduction to advanced topics in Markov chain Monte Carlo (MCMC) algorithms, as applied broadly in the Bayesian computational context. Most, if not all of these topics (stochastic gradient MCMC, non-revers
Externí odkaz:
http://arxiv.org/abs/2407.12751
Recent work has suggested using Monte Carlo methods based on piecewise deterministic Markov processes (PDMPs) to sample from target distributions of interest. PDMPs are non-reversible continuous-time processes endowed with momentum, and hence can mix
Externí odkaz:
http://arxiv.org/abs/2406.19051
Divide-and-conquer MCMC is a strategy for parallelising Markov Chain Monte Carlo sampling by running independent samplers on disjoint subsets of a dataset and merging their output. An ongoing challenge in the literature is to efficiently perform this
Externí odkaz:
http://arxiv.org/abs/2406.11664
Autor:
Carrington, Rachel, Fearnhead, Paul
Quantifying uncertainty in detected changepoints is an important problem. However it is challenging as the naive approach would use the data twice, first to detect the changes, and then to test them. This will bias the test, and can lead to anti-cons
Externí odkaz:
http://arxiv.org/abs/2405.15670
Autor:
Fearnhead, Paul, Fryzlewicz, Piotr
A manuscript version of the chapter "The Multiple Change-in-Gaussian-Mean Problem" from the book "Change-Point Detection and Data Segmentation" by Fearnhead and Fryzlewicz, currently in preparation. All R code and data to accompany this chapter and t
Externí odkaz:
http://arxiv.org/abs/2405.06796
We consider the challenge of efficiently detecting changes within a network of sensors, where we also need to minimise communication between sensors and the cloud. We propose an online, communication-efficient method to detect such changes. The proce
Externí odkaz:
http://arxiv.org/abs/2403.18549
Autor:
Dilillo, Giuseppe, Ward, Kes, Eckley, Idris A., Fearnhead, Paul, Crupi, Riccardo, Evangelista, Yuri, Vacchi, Andrea, Fiore, Fabrizio
We describe how a novel online changepoint detection algorithm, called Poisson-FOCuS, can be used to optimally detect gamma-ray bursts within the computational constraints imposed by miniaturized satellites such as the upcoming HERMES-Pathfinder cons
Externí odkaz:
http://arxiv.org/abs/2312.08817
The increasing volume of data streams poses significant computational challenges for detecting changepoints online. Likelihood-based methods are effective, but a naive sequential implementation becomes impractical online due to high computational cos
Externí odkaz:
http://arxiv.org/abs/2311.01174
Inference for high-dimensional hidden Markov models is challenging due to the exponential-in-dimension computational cost of the forward algorithm. To address this issue, we introduce an innovative composite likelihood approach called "Simulation Bas
Externí odkaz:
http://arxiv.org/abs/2310.10761
Online changepoint detection algorithms that are based on likelihood-ratio tests have been shown to have excellent statistical properties. However, a simple online implementation is computationally infeasible as, at time $T$, it involves considering
Externí odkaz:
http://arxiv.org/abs/2302.04743