Concept

Bayes factor

Related lectures (29)

Supervised Learning with kNN: Regression Model

Covers a simple mathematical model for supervised learning with k-nearest neighbors in regression.

Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.

Bayesian Inference: Optimal Estimation

Explores optimal Bayesian inference, denoising, scalar estimation, and phase transitions.

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Advanced Probability: Bayes' Theorem and Random Variables

Covers advanced probability concepts, including Bayes' Theorem and Random Variables.

Sampling: maximum likelihood estimation MOOC: Selected Topics on Discrete Choice

Explores sampling in maximum likelihood estimation and its implications on the joint probability and likelihood contribution.

Understanding Statistics & Experimental Design

Covers statistics, experimental design, errors, distributions, implications of sample size, and null results.

Mixture models: individual level parameters MOOC: Selected Topics on Discrete Choice

Explores mixture models and individual-level parameters in discrete choice scenarios, covering distribution, Bayes theorem, and expected values.

Sampling: conditional maximum likelihood estimation MOOC: Selected Topics on Discrete Choice

Covers Conditional Maximum Likelihood estimation, contribution to likelihood, and MEV model application in choice-based samples.

Transformations of Joint Densities

Covers the transformations of joint continuous densities and their implications on probability distributions.

Conditional Probability

Explores conditional probability, the law of total probability, Bayes' theorem, and prediction decomposition.

Advanced Probability: Probability Trees and Conditional Probabilities

Explores probability trees, conditional probabilities, Bernoulli trials, binomial distribution, and Bayes' Theorem.

Probability and Statistics: Discrete Random Variables

Explores discrete random variables, mass functions, and distribution functions in probability and statistics.

Bayes' Theorem Overview

Introduces FROG, a tool for educators to enhance classroom interactivity.

Linear Combinations: Moment-Generating Functions

Explores moment-generating functions, linear combinations, and normality of random variables.

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Bayes Theorem: Applications and Interpretation Log in to Mediaspace to watch this video

Explores the practical utility of Bayes Theorem in inverting perspectives and efficiently calculating conditional probabilities.

Nearest Neighbor Classifier: Curse of Dimensionality Log in to Mediaspace to watch this video

Explores the nearest neighbor classifier method, discussing its limitations in high-dimensional spaces and the importance of spatial correlation for effective predictions.

Bayes' Theorem: Applications and Simulations Log in to Mediaspace to watch this video

Covers the application of Bayes' Theorem in practical reasoning, especially in clinical settings.

Bayesian Estimation Log in to Mediaspace to watch this video

Covers the fundamentals of Bayesian estimation, focusing on the application of Bayes' Theorem in scalar estimation.