Why we typically use dependent sampling to sample from the posterior
Explains why independent sampling from the posterior is typically impossible and why we are forced to use dependent sampling instead. This video is part of a lecture course which closely follows the material covered in the book, "A Student's Guide to Bayesian Statistics", published by Sage, which is available to order on Amazon here: https://www.amazon.co.uk/Students-Gui... For more information on all things Bayesian, have a look at: https://ben-lambert.com/bayesian/. The playlist for the lecture course is here: • A Student's Guide to Bayesian Statistics

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What is the difference between independent and dependent sampling algorithms?

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An introduction to importance sampling

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The intuition behind the Hamiltonian Monte Carlo algorithm

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An introduction to Gibbs sampling

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IS CHESS A GAME OF CHANCE? Classical vs Frequentist vs Bayesian Probability

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10. Continuous Bayes' Rule; Derived Distributions

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But what is the Central Limit Theorem?

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Effective sample size: representing the cost of dependent sampling

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Accept-Reject Sampling : Data Science Concepts

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Andrew Gelman: Introduction to Bayesian Data Analysis and Stan with Andrew Gelman

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How to derive a Gibbs sampling routine in general

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An introduction to importance sampling - optimal importance distributions

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Estimating the posterior predictive distribution by sampling

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Something is jamming GPS over Europe. Here's what we found

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Free Event: Power BI Beginner to Pro 2026 Edition - Full Hands-On Tutorial

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Normal Distributions Explained – With Real-World Examples

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Samsung's 990 Pro SSD warranty policy is a scam; I'm taking them to court.

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The Exponential Family (Part 1)

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What do tech pioneers think about the AI revolution? - The Engineers, BBC World Service

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