How can one perform Bayesian inference on stochastic simulators with intractable
likelihoods? A recent approach is to learn the posterior from adaptively proposed …

Recent advances in probabilistic modelling have led to a large number of simulation-based
inference algorithms which do not require numerical evaluation of likelihoods. However, a …

Parameter inference, ie inferring the posterior distribution of the parameters of a statistical
model given some data, is a central problem to many scientific disciplines. Posterior …

Likelihood-Free Inference by Ratio Estimation Page 1 Bayesian Analysis (2022) 17, Number
1, pp. 1–31 Likelihood-Free Inference by Ratio Estimation Owen Thomas ∗ , Ritabrata Dutta † …

Implicit stochastic models, where the data-generation distribution is intractable but sampling
is possible, are ubiquitous in the natural sciences. The models typically have free …

Bayesian inference methods typically require a considerable amount of computation time in
the calculation of forward models. This limitation restricts the application of Bayesian …

Deep feedforward neural networks (DFNNs) are a powerful tool for functional approximation.
We describe flexible versions of generalized linear and generalized linear mixed models …

Many cosmological models have only a finite number of parameters of interest, but a very
expensive data-generating process and an intractable likelihood function. We address the …

Interpreting data using mechanistic mathematical models provides a foundation for
discovery and decision-making in all areas of science and engineering. Develo** …

There is substantial experimental evidence that learning and memory-related behaviours
rely on local synaptic changes, but the search for distinct plasticity rules has been driven by …