Recently, evidence has emerged that humans approach learning using Bayesian updating
rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here …

Little is known about how humans solve the exploitation/exploration trade-off. In particular,
the evidence for uncertainty-driven exploration is mixed. The current study proposes a novel …

The minimum cross-entropy principle is an established technique for design of an unknown
distribution, processing linear functional constraints on the distribution. More generally, fully …

Neoclassical finance assumes that investors are Bayesian. In many realistic situations,
Bayesian learning is challenging. Here, we consider investment opportunities that change …

Optimal transport (OT) has an important role in transforming data distributions in a manner
which engenders fairness. Typically, the OT operators are learnt from the unfair attribute …

This paper exploits knowledge made available by an external source in the form of a
predictive distribution in order to elicit a parameter prior. It uses the terminology of Bayesian …

Probabilistic models of decision making under various forms of uncertainty have been
applied in recent years to numerous behavioral and model-based fMRI studies. These …

In recent years we have seen a handful of work on inference algorithms over non-stationary
data streams. Given their flexibility, Bayesian non-parametric models are a good candidate …

We study learning in a bandit task in which the outcome probabilities of six arms switch
(“jump”) over time. In the experiment, optimal Bayesian learning tracks the jumps by …

We address the problem of merging probabilistic knowledge in a centralized (nonflat)
multiple participant system. Each participant independently reports a known distribution of a …