arXiv research

Proposes LFGP for likelihood-free Gaussian process regression.

ALFI improves likelihood-free inference for black-box generators.

Likelihood-free (a.k.a. simulation-based) inference problems are inverse problems with expensive, or intractable, forward models. ODE inverse problems are commonly treated as likelihood-free, as their forward map has to be numerically approximated by an ODE solver. This, however, is not a fundamental constraint but jus…

Deep Gaussian Processes improve likelihood-free inference for complex distributions.

This study benchmarks likelihood-free inference methods for models with heavy-tailed or discrete data.

EnVAE uses energy score for likelihood-free VAEs, improving image reconstructions.

Paper proposes energy objective for training normalizing flows without determinants.

In likelihood-free settings where likelihood evaluations are intractable, approximate Bayesian computation (ABC) addresses the formidable inference task to discover plausible parameters of simulation programs that explain the observations. However, they demand large quantities of simulation calls. Critically, hyperpara…

Engine for Likelihood-Free Inference (ELFI) is a Python software library for performing likelihood-free inference (LFI). ELFI provides a convenient syntax for arranging components in LFI, such as priors, simulators, summaries or distances, to a network called ELFI graph. The components can be implemented in a wide vari…

Likelihood-free inference is concerned with the estimation of the parameters of a non-differentiable stochastic simulator that best reproduce real observations. In the absence of a likelihood function, most of the existing inference methods optimize the simulator parameters through a handcrafted iterative procedure tha…

A new method improves likelihood-free Bayesian inference by transforming summary statistics and using efficient Variational Bayes.

Unified approach for sequence design combining likelihood-free inference and black-box optimization.

Direct neural ratio estimator for likelihood-free inference.

New method uses neural exponential families for likelihood-free inference.

A new method reduces dimensionality for better likelihood-free parameter estimation.

Likelihood-free methods perform parameter inference in stochastic simulator models where evaluating the likelihood is intractable but sampling synthetic data is possible. One class of methods for this likelihood-free problem uses a classifier to distinguish between pairs of parameter-observation samples generated using…

Proposes ABC method for discrete data, improving likelihood-free inference.

Bayesian neural networks improve likelihood-free inference efficiency.

New algorithms learn latent variable models without tuning, outperforming existing methods.

Improved likelihood-free inference by localizing and refining low-dimensional approximations.

I consider two problems in machine learning and statistics: the problem of estimating the joint probability density of a collection of random variables, known as density estimation, and the problem of inferring model parameters when their likelihood is intractable, known as likelihood-free inference. The contribution o…

Improves likelihood-free inference by using a new sampling approach to avoid biased data collection.

Likelihood-free inference refers to inference when a likelihood function cannot be explicitly evaluated, which is often the case for models based on simulators. Most of the literature is based on sample-based `Approximate Bayesian Computation' methods, but recent work suggests that approaches based on deep neural condi…

We study the parameter estimation problem in mixture models with observational nonidentifiability: the full model (also containing hidden variables) is identifiable, but the marginal (observed) model is not. Hence global maxima of the marginal likelihood are (infinitely) degenerate and predictions of the marginal likel…

Improved MMD estimator for likelihood-free inference.

New method improves likelihood-free parameter estimation in complex models.

New method uses path signatures for efficient likelihood estimation in time-series data.

Automatically learns summary features from time series data for likelihood-free inference.

Parametric statistical models that are implicitly defined in terms of a stochastic data generating process are used in a wide range of scientific disciplines because they enable accurate modeling. However, learning the parameters from observed data is generally very difficult because their likelihood function is typica…

Likelihood-free methods such as approximate Bayesian computation (ABC) have extended the reach of statistical inference to problems with computationally intractable likelihoods. Such approaches perform well for small-to-moderate dimensional problems, but suffer a curse of dimensionality in the number of model parameter…

Optimal algorithm selects biological models without prior info.

A new method extends Bayesian optimization to more models and utilities.

New method for conditional sampling using M-GANs, likely-free inference.

Simplifies inference for simulators with or without tractable likelihoods.

This paper proposes a novel profile likelihood method for estimating the covariance parameters in exploratory factor analysis of high-dimensional Gaussian datasets with fewer observations than number of variables. An implicitly restarted Lanczos algorithm and a limited-memory quasi-Newton method are implemented to deve…

Likelihood-free inference involves inferring parameter values given observed data and a simulator model. The simulator is computer code which takes parameters, performs stochastic calculations, and outputs simulated data. In this work, we view the simulator as a function whose inputs are (1) the parameters and (2) a ve…

We introduce a framework using Generative Adversarial Networks (GANs) for likelihood--free inference (LFI) and Approximate Bayesian Computation (ABC) where we replace the black-box simulator model with an approximator network and generate a rich set of summary features in a data driven fashion. On benchmark data sets, …

This paper proposes a new method to approximate posterior distributions using generative neural networks trained via scoring rule minimization.

We extend recent work (Brehmer, et. al., 2018) that use neural networks as surrogate models for likelihood-free inference. As in the previous work, we exploit the fact that the joint likelihood ratio and joint score, conditioned on both observed and latent variables, can often be extracted from an implicit generative m…

New particle algorithms optimize latent variable models.

An explosion of high-throughput DNA sequencing in the past decade has led to a surge of interest in population-scale inference with whole-genome data. Recent work in population genetics has centered on designing inference methods for relatively simple model classes, and few scalable general-purpose inference techniques…

A new framework bridges classical and machine learning methods for reliable inference from complex models.

In many fields of science, generalized likelihood ratio tests are established tools for statistical inference. At the same time, it has become increasingly common that a simulator (or generative model) is used to describe complex processes that tie parameters $θ$ of an underlying theory and measurement apparatus to hig…

A new method for experimental design focuses on predicting downstream quantities of interest.

New method estimates HMM hidden states efficiently.

Reconstructing the position of an interaction for any dual-phase time projection chamber (TPC) with the best precision is key to directly detecting Dark Matter. Using the likelihood-free framework, a new algorithm to reconstruct the 2-D (x; y) position and the size of the charge signal (e) of an interaction is presente…

EG-LF-MCMC infers posterior densities without likelihoods.

Paper proposes a new method for probabilistic electricity price forecasting.