State–space models (SSMs) are an important modeling framework for analyzing ecological
time series. These hierarchical models are commonly used to model population dynamics …

Abstract We present Sequential Neural Likelihood (SNL), a new method for Bayesian
inference in simulator models, where the likelihood is intractable but simulating data from …

Mechanistic models of single-neuron dynamics have been extensively studied in
computational neuroscience. However, identifying which models can quantitatively …

Posterior inference with an intractable likelihood is becoming an increasingly common task
in scientific domains which rely on sophisticated computer simulations. Typically, these …

Inferring parameters of computational models that capture experimental data is a central
task in cognitive neuroscience. Bayesian statistical inference methods usually require the …

Abstract Approximate Bayesian Computation (ABC) provides methods for Bayesian
inference in simulation-based models which do not permit tractable likelihoods. We present …

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

We present Sequential Neural Variational Inference (SNVI), an approach to perform
Bayesian inference in models with intractable likelihoods. SNVI combines likelihood …

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

The reparameterization trick is widely used in variational inference as it yields more accurate
estimates of the gradient of the variational objective than alternative approaches such as the …