
Generalised Bayes Updates with fdivergences through Probabilistic Classifiers
A stream of algorithmic advances has steadily increased the popularity o...
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LikelihoodFree Inference with Deep Gaussian Processes
In recent years, surrogate models have been successfully used in likelih...
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Adaptive MCMC for synthetic likelihoods and correlated synthetic likelihoods
Approximate Bayesian computation (ABC) and synthetic likelihood (SL) are...
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Probabilistic elicitation of expert knowledge through assessment of computer simulations
We present a new method for probabilistic elicitation of expert knowledg...
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SplitBOLFI for for misspecificationrobust likelihood free inference in high dimensions
Likelihoodfree inference for simulatorbased statistical models has rec...
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Diagnosing model misspecification and performing generalized Bayes' updates via probabilistic classifiers
Model misspecification is a longstanding enigma of the Bayesian inferen...
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Learning pairwise Markov network structures using correlation neighborhoods
Markov networks are widely studied and used throughout multivariate stat...
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Boosting heritability: estimating the genetic component of phenotypic variation with multiple sample splitting
Heritability is a central measure in genetics quantifying how much of th...
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Adaptive Approximate Bayesian Computation Tolerance Selection
Approximate Bayesian Computation (ABC) methods are increasingly used for...
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Composite local lowrank structure in learning drug sensitivity
The molecular characterization of tumor samples by multiple omics data s...
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Metaanalysis of Bayesian analyses
Metaanalysis aims to combine results from multiple related statistical ...
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Highdimensional structure learning of binary pairwise Markov networks: A comparative numerical study
Learning the undirected graph structure of a Markov network from data is...
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ELFI: Engine for Likelihood Free Inference
The Engine for LikelihoodFree Inference (ELFI) is a Python software lib...
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Inferring Cognitive Models from Data using Approximate Bayesian Computation
An important problem for HCI researchers is to estimate the parameter va...
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On the inconsistency of ℓ_1penalised sparse precision matrix estimation
Various ℓ_1penalised estimation methods such as graphical lasso and CLI...
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Learning Gaussian Graphical Models With Fractional Marginal Pseudolikelihood
We propose a Bayesian approximate inference method for learning the depe...
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Fast kNN search
Efficient index structures for fast approximate nearest neighbor queries...
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Classification and Bayesian Optimization for LikelihoodFree Inference
Some statistical models are specified via a data generating process for ...
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Bayesian Optimization for LikelihoodFree Inference of SimulatorBased Statistical Models
Our paper deals with inferring simulatorbased statistical models given ...
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Likelihoodfree inference via classification
Increasingly complex generative models are being used across disciplines...
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Marginal and simultaneous predictive classification using stratified graphical models
An inductive probabilistic classification rule must generally obey the p...
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Marginal PseudoLikelihood Learning of Markov Network structures
Undirected graphical models known as Markov networks are popular for a w...
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Labeled Directed Acyclic Graphs: a generalization of contextspecific independence in directed graphical models
We introduce a novel class of labeled directed acyclic graph (LDAG) mode...
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Stratified Graphical Models  ContextSpecific Independence in Graphical Models
Theory of graphical models has matured over more than three decades to p...
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Jukka Corander
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Professor of biostatistics at the Faculty of Medicine, University of Oslo since 2016, Professor of statistics at University of Helsinki, Finland since 2009, ViceCirector of the COIN Centre of Excellence in computational inference research since 2009, Professor at Wellcome Sanger Institute, Genome Research Limited since 2010.