Frontiers

Computational Frameworks for Decision-Making: From Bayesian Inference to Reinforcement Learning Models

The ability to make adaptive decisions in uncertain environments is a fundamental characteristic of biological intelligence. Historically, computational ...
JSTOR Daily

Bayesian inference for the mixed conditional heteroskedasticity model

We estimate by Bayesian inference the mixed conditional heteroskedasticity model of Haas et al. (2004a Journal of Financial Econometrics 2, 211–50). We construct a Gibbs sampler algorithm to compute ...
JSTOR Daily

Bayesian Detection and Modeling of Spatial Disease Clustering

Many current statistical methods for disease clustering studies are based on a hypothesis testing paradigm. These methods typically do not produce useful estimates of disease rates or cluster risks.
Diginomica

Growing better brains - why we need to re-think the neuron for more trustworthy and efficient AI

Verses demonstrates progress in leveraging AI models using Bayesian networks and active inference that are significantly smaller, more energy efficient, and honest than Deep Neural Network approaches.
lse

Bayesian Inference

This course is available on the BSc in Actuarial Science, BSc in Actuarial Science (with a Placement Year), BSc in Data Science, BSc in Mathematics with Data Science, BSc in Mathematics with Economics ...

Flight Path Data Shows How Mosquitoes Target Humans

Scientists have quantified what draws mosquitoes to people—which could help make better, life-saving bug traps.
Helsinki

Symposium 3: Active Inference as a Complementary Framework for Modeling Motivation and Cognitive Control

Current models of motivation and cognitive control have relied to a large extent on the Reinforcement Learning framework. While this approach has enjoyed considerable success, other frameworks may ...