1 code implementation • 8 Nov 2021 • Santtu Tikka, Jouni Helske, Juha Karvanen
Graphs are commonly used to represent and visualize causal relations.
1 code implementation • 21 Jan 2021 • Jouni Helske, Matti Vihola
We present an R package bssm for Bayesian non-linear/non-Gaussian state space modelling.
Bayesian Inference Computation
1 code implementation • 15 Sep 2020 • Jouni Helske
The R package walker extends standard Bayesian general linear models to the case where the effects of the explanatory variables can vary in time.
Computation
1 code implementation • 6 Mar 2020 • Jouni Helske, Santtu Tikka, Juha Karvanen
This bias is related to variables that we call trapdoor variables.
Methodology Computation
2 code implementations • 17 Feb 2020 • Jouni Helske, Satu Helske, Matthew Cooper, Anders Ynnerman, Lonni Besançon
Common reporting styles for statistical results in scientific articles, such as p-values and confidence intervals (CI), have been reported to be prone to dichotomous interpretations, especially with respect to the null hypothesis significance testing framework.
Other Statistics Human-Computer Interaction
2 code implementations • NeurIPS 2018 • Fredrik Lindsten, Jouni Helske, Matti Vihola
Approximate inference in probabilistic graphical models (PGMs) can be grouped into deterministic methods and Monte-Carlo-based methods.
1 code implementation • 3 Apr 2017 • Satu Helske, Jouni Helske
The seqHMM package in R is designed for the efficient modeling of sequences and other categorical time series data containing one or multiple subjects with one or multiple interdependent sequences using HMMs and MHMMs.
Computation Applications
1 code implementation • 6 Dec 2016 • Jouni Helske
State space modelling is an efficient and flexible method for statistical inference of a broad class of time series and other data.
Computation Methodology
1 code implementation • 8 Sep 2016 • Matti Vihola, Jouni Helske, Jordan Franks
We consider importance sampling (IS) type weighted estimators based on Markov chain Monte Carlo (MCMC) targeting an approximate marginal of the target distribution.
Computation Probability