Computational Cognition Cheat Sheets
Over the years, we've written several notes providing brief introductions to computational methods that are often useful in the study of human cognition. Many undergraduate and graduate students have told us that these notes are extremely helpful.
- Backpropagation Algorithm
- Bayesian Estimation
- Bayesian Inference: Gibbs Sampling | Sample Code
- Bayesian Inference: Metropolis-Hastings Sampling | Sample Code
- Bayesian Inference: Particle Filtering
- Bayesian Statistics: Beta-Binomial Model
- Bayesian Statistics: Dirichlet Processes
- Bayesian Statistics: Indian Buffet Process | Sample Code
- Bayesian Statistics: Normal-Normal Model
- Conditional Independence, Dependency-Separation, and Bayesian Networks
- Factor Analysis
- Hidden Markov Models
- K-Means Algorithm for Clustering
- Maximum Likelihood Estimation
- Mixture Models
- Mixtures-of-Experts
- Optimal Linear Cue Combination
- Principal Components Analysis
- Principal Components Analysis and Unsupervised Hebbian Learning
- Reinforcement Learning: Model-based
- Reinforcement Learning: Model-free
- Sensory Integration and Kalman Filtering
- Variational Autoencoder