Bayes' Theorem: Applications and Simulations

About
Privacy
Disclaimer

Graph Chatbot

In course

Sint officia ea sunt sunt sit pariatur. Ad aliquip ad proident mollit elit esse deserunt cupidatat non consectetur. Minim adipisicing Lorem consectetur consequat nostrud sint qui voluptate voluptate. Ullamco consequat et consequat tempor consequat nisi. Lorem occaecat dolore ullamco Lorem eiusmod dolor ullamco. Tempor proident magna aliqua esse non officia sit elit sint velit. In amet excepteur consequat cupidatat.

Description

This lecture covers the application of Bayes' Theorem in practical reasoning, especially in clinical settings. It explains how to calculate conditional probabilities and discusses Bayesian inference. The instructor introduces simulations to illustrate drug-testing examples.

This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.

Watch on Mediaspace

Official source

Related lectures (28)

https://mediaspace.epfl.ch/media/0_1ihlhp1x

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

In course

DEMO: qui mollit officia

Related lectures (28)

Probability and Statistics: Independence and Conditional Probability

Explores independence and conditional probability in probability and statistics, with examples illustrating the concepts and practical applications.

Probabilistic Linear Regression

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Conditional Probability: Prediction Decomposition

Explores conditional probability, Bayes' theorem, and prediction decomposition for informed decision-making.

Probability and Statistics

Introduces key concepts in probability and statistics, such as events, Venn diagrams, and conditional probability.

Probability and Statistics

Introduces key concepts in probability and statistics, covering random experiments, events, intersections, unions, and more.