5 papers
Neural composite likelihood estimation: simulation based inference for time series
Grace Yan, Mark Beaumont, Dennis Prangle
Simulation based inference (SBI) circumvents the challenge of intractable likelihoods by using a simulator that generates data given parameter values. For instance, neural likeliho…
Minimum Distance Summaries for Robust Neural Posterior Estimation
Sherman Khoo, Dennis Prangle, Song Liu +1
Simulation-based inference (SBI) enables amortized Bayesian inference by first training a neural posterior estimator (NPE) on prior-simulator pairs, typically through low-dimension…
A Comparison of Kernels for ABC-SMC
Dennis Prangle, Cecilia Viscardi, Sammy Ragy
A popular method for likelihood-free inference is approximate Bayesian computation sequential Monte Carlo (ABC-SMC) algorithms. These approximate the posterior using a population o…
Flexible Tails for Normalizing Flows
Tennessee Hickling, Dennis Prangle
Normalizing flows are a flexible class of probability distributions, expressed as transformations of a simple base distribution. A limitation of standard normalizing flows is repre…
Flexible Tails for Normalising Flows, with Application to the Modelling of Financial Return Data
Tennessee Hickling, Dennis Prangle
We propose a transformation capable of altering the tail properties of a distribution, motivated by extreme value theory, which can be used as a layer in a normalizing flow to appr…