no code implementations • 22 Feb 2023 • T. Tien Mai, Gerry Tonkin-Hill, John A. Lees, Jukka Corander

The study of common heritability, or co-heritability, among multiple traits has been widely established in quantitative and molecular genetics.

no code implementations • 8 Jun 2022 • Jukka Corander, Ulpu Remes, Timo Koski

Choice of appropriate structure and parametric dimension of a model in the light of data has a rich history in statistical research, where the first seminal approaches were developed in 1970s, such as the Akaike's and Schwarz's model scoring criteria that were inspired by information theory and embodied the rationale called Occam's razor.

no code implementations • 22 May 2022 • Jukka Corander, Ulpu Remes, Ida Holopainen, Timo Koski

Likelihood-free inference for simulator-based statistical models has recently attracted a surge of interest, both in the machine learning and statistics communities.

no code implementations • 11 Nov 2021 • Rebecca H. Chisholm, Jake A. Lacey, Jan Kokko, Patricia T. Campbell, Malcolm I. McDonald, Jukka Corander, Mark R. Davies, Steven Y. C. Tong, Jodie McVernon, Nicholas Geard

The bacterium Group A Streptococcus (Streptococcus pyogenes, GAS) is a human-specific pathogen and a major cause of global morbidity and mortality.

no code implementations • 6 Oct 2021 • Zhirong Yang, Yuwei Chen, Jukka Corander

Second, we check the assumptions in the clustering guarantee of t-SNE and find they are often violated for real-world data sets.

1 code implementation • 18 Aug 2021 • Zhirong Yang, Yuwei Chen, Denis Sedov, Samuel Kaski, Jukka Corander

In this family, much better cluster visualizations often appear with a parameter value different from the one corresponding to SNE.

no code implementations • 8 Apr 2021 • Marko Järvenpää, Jukka Corander

We present a framework for approximate Bayesian inference when only a limited number of noisy log-likelihood evaluations can be obtained due to computational constraints, which is becoming increasingly common for applications of complex models.

no code implementations • 29 Mar 2021 • Johan Pensar, Henrik Nyman, Jukka Corander

Markov networks are popular models for discrete multivariate systems where the dependence structure of the variables is specified by an undirected graph.

1 code implementation • 18 Jun 2020 • Alexander Aushev, Henri Pesonen, Markus Heinonen, Jukka Corander, Samuel Kaski

In recent years, surrogate models have been successfully used in likelihood-free inference to decrease the number of simulator evaluations.

1 code implementation • 9 Apr 2020 • Umberto Picchini, Umberto Simola, Jukka Corander

Synthetic likelihood (SL) is a strategy for parameter inference when the likelihood function is analytically or computationally intractable.

Methodology

no code implementations • 21 Feb 2020 • Owen Thomas, Raquel Sá-Leão, Hermínia de Lencastre, Samuel Kaski, Jukka Corander, Henri Pesonen

To advance the possibilities for performing likelihood-free inference in higher dimensional parameter spaces, we introduce an extension of the popular Bayesian optimisation based approach to approximate discrepancy functions in a probabilistic manner which lends itself to an efficient exploration of the parameter space.

no code implementations • 12 Dec 2019 • Owen Thomas, Jukka Corander

Here we show how probabilistic classifiers can be employed to resolve this issue.

1 code implementation • 30 Oct 2019 • Juri Kuronen, Jukka Corander, Johan Pensar

Markov networks are widely studied and used throughout multivariate statistics and computer science.

no code implementations • 14 Jan 2019 • Johan Pensar, Yingying Xu, Santeri Puranen, Maiju Pesonen, Yoshiyuki Kabashima, Jukka Corander

Learning the undirected graph structure of a Markov network from data is a problem that has received a lot of attention during the last few decades.

2 code implementations • 2 Aug 2017 • Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén, Marko Järvenpää, Pekka Marttinen, Michael U. Gutmann, Aki Vehtari, Jukka Corander, Samuel Kaski

The stand-alone ELFI graph can be used with any of the available inference methods without modifications.

no code implementations • 2 Dec 2016 • Antti Kangasrääsiö, Kumaripaba Athukorala, Andrew Howes, Jukka Corander, Samuel Kaski, Antti Oulasvirta

An important problem for HCI researchers is to estimate the parameter values of a cognitive model from behavioral data.

1 code implementation • 30 Nov 2016 • Owen Thomas, Ritabrata Dutta, Jukka Corander, Samuel Kaski, Michael U. Gutmann

The popular synthetic likelihood approach infers the parameters by modelling summary statistics of the data by a Gaussian probability distribution.

1 code implementation • 7 Sep 2016 • Yao Lu, Jukka Corander, Zhirong Yang

To solve this problem, we introduce a fast normalization method and normalize the similarity matrix to be doubly stochastic such that all the data points have equal total similarities.

no code implementations • 8 Mar 2016 • Otte Heinävaara, Janne Leppä-aho, Jukka Corander, Antti Honkela

Various $\ell_1$-penalised estimation methods such as graphical lasso and CLIME are widely used for sparse precision matrix estimation.

no code implementations • 25 Feb 2016 • Janne Leppä-aho, Johan Pensar, Teemu Roos, Jukka Corander

We propose a Bayesian approximate inference method for learning the dependence structure of a Gaussian graphical model.

1 code implementation • 23 Sep 2015 • Ville Hyvönen, Teemu Pitkänen, Sotiris Tasoulis, Elias Jääsaari, Risto Tuomainen, Liang Wang, Jukka Corander, Teemu Roos

The method is straightforward to implement using sparse projections which leads to a reduced memory footprint and fast index construction.

no code implementations • 19 Feb 2015 • Michael U. Gutmann, Jukka Corander, Ritabrata Dutta, Samuel Kaski

This approach faces at least two major difficulties: The first difficulty is the choice of the discrepancy measure which is used to judge whether the simulated data resemble the observed data.

no code implementations • 14 Jan 2015 • Michael U. Gutmann, Jukka Corander

The strategy is implemented using Bayesian optimization and is shown to accelerate the inference through a reduction in the number of required simulations by several orders of magnitude.

no code implementations • 9 Sep 2014 • Henrik Nyman, Johan Pensar, Timo Koski, Jukka Corander

Log-linear models are the popular workhorses of analyzing contingency tables.

no code implementations • 18 Jul 2014 • Michael U. Gutmann, Ritabrata Dutta, Samuel Kaski, Jukka Corander

Increasingly complex generative models are being used across disciplines as they allow for realistic characterization of data, but a common difficulty with them is the prohibitively large computational cost to evaluate the likelihood function and thus to perform likelihood-based statistical inference.

no code implementations • 31 Jan 2014 • Henrik Nyman, Jie Xiong, Johan Pensar, Jukka Corander

An inductive probabilistic classification rule must generally obey the principles of Bayesian predictive inference, such that all observed and unobserved stochastic quantities are jointly modeled and the parameter uncertainty is fully acknowledged through the posterior predictive distribution.

no code implementations • 20 Jan 2014 • Johan Pensar, Henrik Nyman, Juha Niiranen, Jukka Corander

Traditionally, learning of the network structure has been done under the assumption of chordality which ensures that efficient scoring methods can be used.

no code implementations • 4 Oct 2013 • Johan Pensar, Henrik Nyman, Timo Koski, Jukka Corander

We introduce a novel class of labeled directed acyclic graph (LDAG) models for finite sets of discrete variables.

no code implementations • NeurIPS 2013 • Jukka Corander, Tomi Janhunen, Jussi Rintanen, Henrik Nyman, Johan Pensar

We investigate the problem of learning the structure of a Markov network from data.

no code implementations • 25 Sep 2013 • Henrik Nyman, Johan Pensar, Timo Koski, Jukka Corander

Theory of graphical models has matured over more than three decades to provide the backbone for several classes of models that are used in a myriad of applications such as genetic mapping of diseases, credit risk evaluation, reliability and computer security, etc.

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