Computer scientists have long recognized that naive implementations of algorithms often
result in a paralyzing degree of redundant computation. More sophisticated implementations …

Automated, data-driven construction and evaluation of scientific models and theories is a
long-standing challenge in artificial intelligence. We present a framework for algorithmically …

Bayesian theories of cognition assume that people can integrate probabilities rationally.
However, several empirical findings contradict this proposition: human probabilistic …

Perception emerges from unconscious probabilistic inference, which guides behaviour in
our ubiquitously uncertain environment. Bayesian decision theory is a prominent …

The method of choice for integrating the time-dependent Fokker–Planck equation (FPE) in
high-dimension is to generate samples from the solution via integration of the associated …

We cast the resampling step in particle filters (PFs) as a variational inference problem,
resulting in a new class of resampling schemes: variational resampling. Variational …

Bayesian models of cognition assume that people compute probability distributions over
hypotheses. However, the required computations are frequently intractable or prohibitively …

In variational inference (VI), the marginal log-likelihood is estimated using the standard
evidence lower bound (ELBO), or improved versions as the importance weighted ELBO …

How do humans search for rewards? This question is commonly studied using multi-armed
bandit tasks, which require participants to trade off exploration and exploitation. Standard …

In the real world, complex dynamic scenes often arise from the composition of simpler parts.
The visual system exploits this structure by hierarchically decomposing dynamic scenes …