NOC:Bayesian Data Analysis for the Behavioral Sciences

Lecture 1 - Sets

Lecture 2 - Probability Axioms

Lecture 3 - Probability Assignment

Lecture 4 - Probability Assigner Functions

Lecture 5 - Random Variables

Lecture 6 - Probability Distributions and Statistical Inference

Lecture 7 - Discrete Random variables: Binomial and Poisson Distributions

Lecture 8 - Continuous Random Variables

Lecture 9 - Normal Distribution

Lecture 10 - Lognormal Distribution and Beta Distribution

Lecture 11 - Probability Distributions in R

Lecture 12 - Likelihood Function

Lecture 13 - Graph of a Likelihood Function

Lecture 14 - Conditional Probability

Lecture 15 - Bayes' Theorem

Lecture 16 - Bayesian Inference

Lecture 17 - Likelihood, Prior and Posterior in R

Lecture 18 - How the data, prior affects the posterior

Lecture 19 - Prior and Posterior Predictions

Lecture 20 - Generating Prior and Posterior Prediction in R

Lecture 21 - Posterior Predictive Checks

Lecture 22 - Model Building in the Bayesian Framework

Lecture 23 - Bayesian Modeling Workflow in R

Lecture 24 - Bayesian Learning

Lecture 25 - Specifying Priors

Lecture 26 - Parameter Estimation Method

Lecture 27 - Analytical Methods of Posterior Estimation

Lecture 28 - Grid Approximation

Lecture 29 - Importance Sampling

Lecture 30 - Introduction to Markov Chain Monte Carlo

Lecture 31 - MCMC Sampler

Lecture 32 - MCMC Implementation

Lecture 33 - MCMC Diagnostics

Lecture 34 - MCMC Examples

Lecture 35 - MCMC Sampling for Data with Multiple Observations

Lecture 36 - Hamiltonian Monte Carlo

Lecture 37 - HMC Implementation

Lecture 38 - HMC Example

Lecture 39 - Introduction to brms

Lecture 40 - Parameter Estimation using brms

Lecture 41 - Introduction to Regression Models

Lecture 42 - Regression Modeling using brms

Lecture 43 - Logistic and Poisson Regression

Lecture 44 - Regression Modelling Examples

Lecture 45 - Interaction Effect and Nested Contrast

Lecture 46 - Posterior Predictive Checks

Lecture 47 - Information- Theoretic Measures of Predictive Accuracy

Lecture 48 - Expected Log Predictive Density (ELPD)

Lecture 49 - Cross Validation

Lecture 50 - Cross Validation Implementation

Lecture 51 - Validations with Brms

Lecture 52 - Bayes Factor

Lecture 53 - Bayes factor Implementation

Lecture 54 - Hypothesis Testing using Bayes Factors

Lecture 55 - Computing Bayes Factor using Bridge Sampling

Lecture 56 - Bayesian Hierarchical Models

Lecture 57 - Hierarchical Regression Models

Lecture 58 - ndividual Difference Models

Lecture 59 - Hierarchical Modeling with brms

Lecture 60 - Theory Testing and Computational Modeling