We propose a novel method (floz), based on normalizing flows, to estimate the Bayesian
evidence (and its numerical uncertainty) from a preexisting set of samples drawn from the …

Measurement reliability is a fundamental concept in psychology. It is traditionally considered
a stable property of a questionnaire, measurement device, or experimental task. Although …

Research in psychology has seen a rapid increase in the usage of experience sampling
methods and daily diary methods. The data that result from using these methods are …

We introduce a new copula-based correction for generalized linear mixed models (GLMMs)
within the integrated nested Laplace approximation (INLA) approach for approximate …

We propose the approximate Laplace approximation (ALA) to evaluate integrated
likelihoods, a bottleneck in Bayesian model selection. The Laplace approximation (LA) is a …

A reciprocal LASSO (rLASSO) regularization employs a decreasing penalty function as
opposed to conventional penalization approaches that use increasing penalties on the …

Various computational challenges arise when applying Bayesian inference approaches to
complex hierarchical models. Sampling-based inference methods, such as Markov Chain …

Routinely-implemented deterministic approximations of posterior distributions from, eg,
Laplace method, variational Bayes and expectation-propagation, generally rely on …

We provide a theoretical and computational investigation of the Gamma-Maximin method
with soft revision, which was recently proposed as a robust criterion for pseudo-label …

We present two approximate Bayesian inference methods for parameter estimation in partial
differential equation (PDE) models with space-dependent and state-dependent parameters …