Probabilistic graphical models have emerged as a powerful modeling tool for several real-
world scenarios where one needs to reason under uncertainty. A graphical model's partition …

The field of reinforcement learning can be split into model-based and model-free methods.
Here, we unify these approaches by casting model-free policy optimisation as amortised …

Stochastic optimization techniques are standard in variational inference algorithms. These
methods estimate gradients by approximating expectations with independent Monte Carlo …

This thesis considers the free energy principle (FEP) and its corollary, active inference,
which form an explanatory framework that prescribes a Bayesian interpretation of self …

Despite the growth of data size, many applications for which we would like to apply learning
algorithms to are limited by data quantity and quality. Generative models propose a …