Matti Vihola
@mattivihola.bsky.social
450 followers
220 following
11 posts
Professor of Statistics, University of Jyväskylä. Computational statistics, applied probability, Monte Carlo methods, Bayesian inference.
https://iki.fi/mvihola/
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Reposted by Matti Vihola
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Sam Power
@spmontecarlo.bsky.social
· Sep 2
Reposted by Matti Vihola
Reposted by Matti Vihola
Matti Vihola
@mattivihola.bsky.social
· Jun 2
Mixing time of the conditional backward sampling particle filter
The conditional backward sampling particle filter (CBPF) is a powerful Markov chain Monte Carlo sampler for general state space hidden Markov model (HMM) smoothing. It was proposed as an improvement o...
arxiv.org
Reposted by Matti Vihola
Mattias Villani
@matvil.bsky.social
· May 8
Senior Lecturer in Statistics, specialized in Data Science
For more information about us, please visit: Department of Statistics Subject/subject description Data science is a multidisciplinary academic field that uses scientific methods, algorithms and system
bit.ly
Reposted by Matti Vihola
Vera Mikkilä
@veramikkila.bsky.social
· Apr 16
Finland invests in research excellence – new funding call by Research Council of Finland to support recruitment of international talents
The Research Council of Finland (RCF) has launched a funding call to improve universities’ ability to recruit international experts to Finland. In line with the RCF’s strategy, the call wi...
www.aka.fi
Reposted by Matti Vihola
Reposted by Matti Vihola
Matti Vihola
@mattivihola.bsky.social
· Feb 12
Conditional particle filters with diffuse initial distributions - Statistics and Computing
Conditional particle filters (CPFs) are powerful smoothing algorithms for general nonlinear/non-Gaussian hidden Markov models. However, CPFs can be inefficient or difficult to apply with diffuse initi...
doi.org
Matti Vihola
@mattivihola.bsky.social
· Feb 12
Matti Vihola
@mattivihola.bsky.social
· Feb 6
Matti Vihola
@mattivihola.bsky.social
· Feb 5
Adaptive Gibbs samplers and related MCMC methods
We consider various versions of adaptive Gibbs and Metropolis-within-Gibbs samplers, which update their selection probabilities (and perhaps also their proposal distributions) on the fly during a run ...
doi.org
Reposted by Matti Vihola
Reposted by Matti Vihola
Rui-Yang Zhang
@ryzhang.bsky.social
· Jan 29
Reposted by Matti Vihola
Reposted by Matti Vihola