Summer Schools

Introduction to Probabilistic Models of Cognition

Basic Bayes: model fitting, model selection, and model averaging

From deduction and symbols to induction and probabilities

Introduction to Graphical Models

Bayesian Decision Theory

Information Theory

Graphical Models in Vision

HMMs and inference in graphical models I

Ideal Observers and Ideal Actors

Comparisons between human performance and model predictions in motion perception

HMMs and inference in graphical models II

Sensory Cue Combination

Probabilistic generative models and unsupervised learning I

Probabilistic generative models and unsupervised learning II

Nonparametric Bayesian Models

Modern Monte Carlo methods I: Importance sampling and sequential Monte Carlo

Modern Monte Carlo methods II: Markov chain Monte Carlo

Bayesian supervised learning and semi-supervised learning

Hierarchical Bayesian Models

Hierarchical Bayesian models of human inductive learning

Information theory, minimum description length, and human cognition

Causal Models

Graphical models and human causal learning

Model selection versus parameter estimation in Bayesian inference: An application to causal learning

Causal Reasoning in Humans and Rats

Causal Learning in Children

Modeling causal learning in children

From Causal Models to Analogical Inference

Probability for Worlds with Things in Them

Relational Probability Models

Unknown Objects and BLOG

First-order probabilistic models in human cognition

Learning relational theories

Hierarchy and Reusability in Image Analysis I

Hierarchy and Reusability in Image Analysis II

Image Parsing

Discovering Meaning in the Visual World

Human Object Perception: Bottom-up and Top-down

An introduction to grammars and parsing for language 1

An introduction to grammars and parsing for language 2
Updated Presentation

Probabilistic sentence processing 1

Probabilistic sentence processing 2

Semantic Representations with Probabilistic Topic Models

Models of conditioning

Locally Bayesian Learning

Advanced conditioning: competition and graphical models

Reinforcement Learning

Bayesian Decision Theory and Sequential Sampling

Neural Substrates for Conditioning

Neural representation of value, reward and expectation

Reinforcement learning in human neuroimaging; uncertainty-based competition

Hierarchical reinforcement learning

Models of human action understanding and social cognition as inverse decision-making

Statistical decision theory as a framework for cognition and action.

Neuro- and behavioral economic analysis of human choice and learning

Using Decision Theory to understand Motor Control

Rational analysis of human memory and prediction

Bayesian models of memory retrieval

Grammar induction in language

Grammer induction in vision

Cognitive development as a computational challenge: the grammar analogy

The development of structured representations

Development of Causal Theories

Learning to be causal

Causal Inference in Motor Learning and Adaptation

Multisensory integration-segregation as Bayesian inference

Challenges for Bayes