A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
ALFI improves likelihood-free inference for black-box generators.
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.
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…
Unified approach for sequence design combining likelihood-free inference and black-box optimization.
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…
Bayesian neural networks improve likelihood-free inference efficiency.
Improved MMD estimator for likelihood-free inference.
A new method improves likelihood-free Bayesian inference by transforming summary statistics and using efficient Variational Bayes.
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…
Automatically learns summary features from time series data for likelihood-free inference.
Improves likelihood-free inference by using a new sampling approach to avoid biased data collection.
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…
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, …
Proposes ABC method for discrete data, improving likelihood-free inference.
New method uses neural exponential families for likelihood-free inference.
A new framework bridges classical and machine learning methods for reliable inference from complex models.
A new method extends Bayesian optimization to more models and utilities.
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…
Optimal algorithm selects biological models without prior info.
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…
New method for conditional sampling using M-GANs, likely-free inference.
New method for state inference in state-space models with unknown dynamics.
New method estimates HMM hidden states efficiently.
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…
Direct neural ratio estimator for likelihood-free inference.
Likelihood-free inference for simulator-based statistical models has developed rapidly from its infancy to a useful tool for practitioners. However, models with more than a handful of parameters still generally remain a challenge for the Approximate Bayesian Computation (ABC) based inference. To advance the possibiliti…
How can one perform Bayesian inference on stochastic simulators with intractable likelihoods? A recent approach is to learn the posterior from adaptively proposed simulations using neural network-based conditional density estimators. However, existing methods are limited to a narrow range of proposal distributions or r…
A new method reduces dimensionality for better likelihood-free parameter estimation.
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…
Our paper deals with inferring simulator-based statistical models given some observed data. A simulator-based model is a parametrized mechanism which specifies how data are generated. It is thus also referred to as generative model. We assume that only a finite number of parameters are of interest and allow the generat…
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…
The paper proposes using path signatures for better inference in time series data.
Approximate Bayesian Computation (ABC) is a framework for performing likelihood-free posterior inference for simulation models. Stochastic Variational inference (SVI) is an appealing alternative to the inefficient sampling approaches commonly used in ABC. However, SVI is highly sensitive to the variance of the gradient…
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…
Improved likelihood-free inference using preconditioned neural posterior estimation.
New method improves likelihood-free parameter estimation in complex models.
The paper tackles hypothesis testing for likelihood-free inference with a new kernel-based approach.
This paper introduces NPR, a technique to improve Bayesian inference for multi-modal, high-dimensional simulations.
EnVAE uses energy score for likelihood-free VAEs, improving image reconstructions.
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. A likelihood-free…
Simplifies inference for simulators with or without tractable likelihoods.
Neural point estimators improve parameter estimation from replicated data.
New method uses path signatures for efficient likelihood estimation in time-series data.
EG-LF-MCMC infers posterior densities without likelihoods.
Performing inference over simulators is generally intractable as their runtime means we cannot compute a marginal likelihood. We develop a likelihood-free inference method to infer parameters for a cardiac simulator, which replicates electrical flow through the heart to the body surface. We improve the fit of a state-o…