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Found 9 papers, 4 papers with code
An Extendable Python Implementation of Robust Optimisation Monte Carlo
Our implementation can be used in two ways.
Enhanced gradient-based MCMC in discrete spaces
The recent introduction of gradient-based MCMC for discrete spaces holds great promise, and comes with the tantalising possibility of new discrete counterparts to celebrated continuous methods such as MALA and HMC.
Extending the statistical software package Engine for Likelihood-Free Inference
Approximate Bayesian Computation (ABC) methods, also known as likelihood-free inference techniques, are a class of models used for performing inference when the likelihood is intractable.
Neural Approximate Sufficient Statistics for Implicit Models
We consider the fundamental problem of how to automatically construct summary statistics for implicit generative models where the evaluation of the likelihood function is intractable, but sampling data from the model is possible.
Parallel Gaussian process surrogate Bayesian inference with noisy likelihood evaluations
We consider Bayesian inference when only a limited number of noisy log-likelihood evaluations can be obtained.
Efficient Bayesian Experimental Design for Implicit Models
Bayesian experimental design involves the optimal allocation of resources in an experiment, with the aim of optimising cost and performance.
Variational Noise-Contrastive Estimation
The core idea is to use a variational lower bound to the NCE objective function, which can be optimised in the same fashion as the evidence lower bound (ELBO) in standard variational inference (VI).
Gaussian process modeling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria
Approximate Bayesian computation (ABC) can be used for model fitting when the likelihood function is intractable but simulating from the model is feasible.
Bregman divergence as general framework to estimate unnormalized statistical models
We show that the Bregman divergence provides a rich framework to estimate unnormalized statistical models for continuous or discrete random variables, that is, models which do not integrate or sum to one, respectively.
