Results 11 to 20 of about 167,396 (260)
, 2015 The theory of learning under the uniform distribution is rich and deep, with connections to cryptography, computational complexity, and the analysis of boolean functions to name a few areas.
Kanade, Varun, Mossel, Elchanan
core +2 more sources
Annual Review of Statistics and Its Application, 2022 Markov chain Monte Carlo is the engine of modern Bayesian statistics, being used to approximate the posterior and derived quantities of interest. Despite this, the issue of how the output from a Markov chain is postprocessed and reported is often overlooked.
South, Leah +3 more
openaire +5 more sources
Accelerating MCMC algorithms [PDF]
WIREs Computational Statistics, 2018 Markov chain Monte Carlo algorithms are used to simulate from complex statistical distributions by way of a local exploration of these distributions. This local feature avoids heavy requests on understanding the nature of the target, but it also potentially induces a lengthy exploration of this target, with a requirement on the number of simulations ...
Robert, Christian P. +3 more
openaire +5 more sources
Monthly Notices of the Royal Astronomical Society, 2021 ABSTRACTWe introduce Bilby-MCMC, a Markov chain Monte Carlo sampling algorithm tuned for the analysis of gravitational waves from merging compact objects. Bilby-MCMC provides a parallel-tempered ensemble Metropolis-Hastings sampler with access to a block-updating proposal library including problem-specific and machine learning proposals. We demonstrate
G Ashton, C Talbot
openaire +3 more sources
Fast Compression of MCMC Output [PDF]
Entropy, 2021 We propose cube thinning, a novel method for compressing the output of an MCMC (Markov chain Monte Carlo) algorithm when control variates are available. It allows resampling of the initial MCMC sample (according to weights derived from control variates), while imposing equality constraints on the averages of these control variates, using the cube ...
Nicolas Chopin, Gabriel Ducrocq
openaire +6 more sources
Multilevel Delayed Acceptance MCMC
SIAM/ASA Journal on Uncertainty Quantification, 2023 29 pages, 12 ...
M. B. Lykkegaard +4 more
openaire +2 more sources
Population pharmacokinetics analysis in Lixoft Monolix softwares
Фармакокинетика и Фармакодинамика, 2022 The article has discussed a step-by-step algorithm for developing population pharmacokinetics models in the Lixoft Monolix software. Features of population pharmacokinetics study’s methodology have described.
A. I. Platova
doaj +1 more source
Detecting recombination with MCMC [PDF]
Bioinformatics, 2002 Abstract Motivation: We present a statistical method for detecting recombination, whose objective is to accurately locate the recombinant breakpoints in DNA sequence alignments of small numbers of taxa (4 or 5). Our approach explicitly models the sequence of phylogenetic tree topologies along a multiple sequence alignment.
Dirk, Husmeier, Gráinne, McGuire
openaire +2 more sources
MCMC‐driven importance samplers
Applied Mathematical Modelling, 2022 Monte Carlo sampling methods are the standard procedure for approximating complicated integrals of multidimensional posterior distributions in Bayesian inference. In this work, we focus on the class of Layered Adaptive Importance Sampling (LAIS) scheme, which is a family of adaptive importance samplers where Markov chain Monte Carlo algorithms are ...
F. Llorente +4 more
openaire +4 more sources
Locking phenomena in finite element analysis of deep beam and removal method
Vietnam Journal of Mechanics, 2005 Since earlier 30 years ago, Finite Element Methods (FEM) has become an indispensable tool of engineers for analysis mechanical behaviour of structures. Generally displacement models are used in most usual problems.
Nguyen Thang Hoa, Tran Ich Thinh
doaj +1 more source