Search Results for author: Jouni Helske

Found 8 papers, 8 papers with code

bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R

1 code implementation21 Jan 2021 Jouni Helske, Matti Vihola

We present an R package bssm for Bayesian non-linear/non-Gaussian state space modelling.

Bayesian Inference Computation

Efficient Bayesian generalized linear models with time-varying coefficients: The walker package in R

1 code implementation15 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

Estimation of causal effects with small data in the presence of trapdoor variables

1 code implementation6 Mar 2020 Jouni Helske, Santtu Tikka, Juha Karvanen

This bias is related to variables that we call trapdoor variables.

Methodology Computation

Can visualization alleviate dichotomous thinking? Effects of visual representations on the cliff effect

2 code implementations17 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

Graphical model inference: Sequential Monte Carlo meets deterministic approximations

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.

Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R

1 code implementation3 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

KFAS: Exponential Family State Space Models in R

1 code implementation6 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

Importance sampling type estimators based on approximate marginal MCMC

1 code implementation8 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

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