9 citations · 9 across the 4 of their papers we have counts for
4 papers
On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction
Matti Vihola, Jordan Franks
Approximate Bayesian computation allows for inference of complicated probabilistic models with intractable likelihoods using model simulations. The Markov chain Monte Carlo impleme…
Graphical model inference: Sequential Monte Carlo meets deterministic approximations
Fredrik Lindsten, Jouni Helske, Matti Vihola
Approximate inference in probabilistic graphical models (PGMs) can be grouped into deterministic methods and Monte-Carlo-based methods. The former can often provide accurate and ra…
Stochastic order characterization of uniform integrability and tightness
Lasse Leskelä, Matti Vihola
We show that a family of random variables is uniformly integrable if and only if it is stochastically bounded in the increasing convex order by an integrable random variable. This…
Robust adaptive Metropolis algorithm with coerced acceptance rate
Matti Vihola
The adaptive Metropolis (AM) algorithm of Haario, Saksman and Tamminen [Bernoulli 7 (2001) 223-242] uses the estimated covariance of the target distribution in the proposal distrib…