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08/09/2022
Inferring the heritability of bacterial traits in the era of machine learning
Quantification of heritability is a fundamental aim in genetics, providi...
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06/08/2022
Likelihood-free Model Choice for Simulator-based Models with the Jensen–Shannon Divergence
Choice of appropriate structure and parametric dimension of a model in t...
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05/22/2022
Nonparametric likelihood-free inference with Jensen-Shannon divergence for simulator-based models with categorical output
Likelihood-free inference for simulator-based statistical models has rec...
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03/23/2022
On predictive inference for intractable models via approximate Bayesian computation
Approximate Bayesian computation (ABC) is commonly used for parameter es...
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12/23/2021
ABC of the Future
Approximate Bayesian computation (ABC) has advanced in two decades from ...
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12/01/2021
Prior knowledge elicitation: The past, present, and future
Specification of the prior distribution for a Bayesian model is a centra...
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10/06/2021
T-SNE Is Not Optimized to Reveal Clusters in Data
Cluster visualization is an essential task for nonlinear dimensionality ...
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08/18/2021
Stochastic Cluster Embedding
Neighbor Embedding (NE) that aims to preserve pairwise similarities betw...
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04/08/2021
Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC
We present an efficient approach for doing approximate Bayesian inferenc...
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03/29/2021
Structure Learning of Contextual Markov Networks using Marginal Pseudo-likelihood
Markov networks are popular models for discrete multivariate systems whe...
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11/03/2020
High-dimensional structure learning of sparse vector autoregressive models using fractional marginal pseudo-likelihood
Learning vector autoregressive models from multivariate time series is c...
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07/08/2020
Generalised Bayes Updates with f-divergences through Probabilistic Classifiers
A stream of algorithmic advances has steadily increased the popularity o...
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06/18/2020
Likelihood-Free Inference with Deep Gaussian Processes
In recent years, surrogate models have been successfully used in likelih...
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04/09/2020
Adaptive MCMC for synthetic likelihoods and correlated synthetic likelihoods
Approximate Bayesian computation (ABC) and synthetic likelihood (SL) are...
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02/25/2020
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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02/21/2020
Split-BOLFI for for misspecification-robust likelihood free inference in high dimensions
Likelihood-free inference for simulator-based statistical models has rec...
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12/12/2019
Diagnosing model misspecification and performing generalized Bayes' updates via probabilistic classifiers
Model misspecification is a long-standing enigma of the Bayesian inferen...
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10/30/2019
Learning pairwise Markov network structures using correlation neighborhoods
Markov networks are widely studied and used throughout multivariate stat...
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10/25/2019
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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06/21/2019
Adaptive Approximate Bayesian Computation Tolerance Selection
Approximate Bayesian Computation (ABC) methods are increasingly used for...
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04/30/2019
Composite local low-rank structure in learning drug sensitivity
The molecular characterization of tumor samples by multiple omics data s...
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04/09/2019
Meta-analysis of Bayesian analyses
Meta-analysis aims to combine results from multiple related statistical ...
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01/14/2019
High-dimensional 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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08/02/2017
ELFI: Engine for Likelihood Free Inference
The Engine for Likelihood-Free Inference (ELFI) is a Python software lib...
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12/02/2016
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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03/08/2016
On the inconsistency of â„“_1-penalised sparse precision matrix estimation
Various â„“_1-penalised estimation methods such as graphical lasso and CLI...
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02/25/2016
Learning Gaussian Graphical Models With Fractional Marginal Pseudo-likelihood
We propose a Bayesian approximate inference method for learning the depe...
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09/23/2015
Fast k-NN search
Efficient index structures for fast approximate nearest neighbor queries...
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02/19/2015
Classification and Bayesian Optimization for Likelihood-Free Inference
Some statistical models are specified via a data generating process for ...
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01/14/2015
Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models
Our paper deals with inferring simulator-based statistical models given ...
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07/18/2014
Likelihood-free inference via classification
Increasingly complex generative models are being used across disciplines...
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01/31/2014
Marginal and simultaneous predictive classification using stratified graphical models
An inductive probabilistic classification rule must generally obey the p...
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01/20/2014
Marginal Pseudo-Likelihood Learning of Markov Network structures
Undirected graphical models known as Markov networks are popular for a w...
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10/04/2013
Labeled Directed Acyclic Graphs: a generalization of context-specific independence in directed graphical models
We introduce a novel class of labeled directed acyclic graph (LDAG) mode...
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09/25/2013