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Found 30 papers, 8 papers with code
Quantifying the common genetic variability of bacterial traits
The study of common heritability, or co-heritability, among multiple traits has been widely established in quantitative and molecular genetics.
Likelihood-free Model Choice for Simulator-based Models with the Jensen--Shannon Divergence
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.
Nonparametric likelihood-free inference with Jensen-Shannon divergence for simulator-based models with categorical output
Likelihood-free inference for simulator-based statistical models has recently attracted a surge of interest, both in the machine learning and statistics communities.
Global and local epidemiology of Group A Streptococcus indicates that naturally-acquired immunity is enduring and strain-specific
The bacterium Group A Streptococcus (Streptococcus pyogenes, GAS) is a human-specific pathogen and a major cause of global morbidity and mortality.
T-SNE Is Not Optimized to Reveal Clusters in Data
Second, we check the assumptions in the clustering guarantee of t-SNE and find they are often violated for real-world data sets.
Stochastic Cluster Embedding
In this family, much better cluster visualizations often appear with a parameter value different from the one corresponding to SNE.
Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC
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.
Structure Learning of Contextual Markov Networks using Marginal Pseudo-likelihood
Markov networks are popular models for discrete multivariate systems where the dependence structure of the variables is specified by an undirected graph.
Likelihood-Free Inference with Deep Gaussian Processes
In recent years, surrogate models have been successfully used in likelihood-free inference to decrease the number of simulator evaluations.
Sequentially guided MCMC proposals for synthetic likelihoods and correlated synthetic likelihoods
Synthetic likelihood (SL) is a strategy for parameter inference when the likelihood function is analytically or computationally intractable.
Methodology
Misspecification-robust likelihood-free inference in high dimensions
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.
Diagnosing model misspecification and performing generalized Bayes' updates via probabilistic classifiers
Here we show how probabilistic classifiers can be employed to resolve this issue.
Learning pairwise Markov network structures using correlation neighborhoods
Markov networks are widely studied and used throughout multivariate statistics and computer science.
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 a problem that has received a lot of attention during the last few decades.
ELFI: Engine for Likelihood-Free Inference
The stand-alone ELFI graph can be used with any of the available inference methods without modifications.
Inferring Cognitive Models from Data using Approximate Bayesian Computation
An important problem for HCI researchers is to estimate the parameter values of a cognitive model from behavioral data.
Likelihood-free inference by ratio estimation
The popular synthetic likelihood approach infers the parameters by modelling summary statistics of the data by a Gaussian probability distribution.
Doubly Stochastic Neighbor Embedding on Spheres
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.
On the inconsistency of $\ell_1$-penalised sparse precision matrix estimation
Various $\ell_1$-penalised estimation methods such as graphical lasso and CLIME are widely used for sparse precision matrix estimation.
Learning Gaussian Graphical Models With Fractional Marginal Pseudo-likelihood
We propose a Bayesian approximate inference method for learning the dependence structure of a Gaussian graphical model.
Fast k-NN search
The method is straightforward to implement using sparse projections which leads to a reduced memory footprint and fast index construction.
Classification and Bayesian Optimization for Likelihood-Free Inference
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.
Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models
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.
Context-specific independence in graphical log-linear models
Log-linear models are the popular workhorses of analyzing contingency tables.
Likelihood-free inference via classification
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.
Marginal and simultaneous predictive classification using stratified graphical models
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.
Marginal Pseudo-Likelihood Learning of Markov Network structures
Traditionally, learning of the network structure has been done under the assumption of chordality which ensures that efficient scoring methods can be used.
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) models for finite sets of discrete variables.
Learning Chordal Markov Networks by Constraint Satisfaction
We investigate the problem of learning the structure of a Markov network from data.
Stratified Graphical Models - Context-Specific Independence in Graphical Models
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.
