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Udemy - Bayesian Statistics - A Step-by-step Introduction

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Udemy - Bayesian Statistics - A Step-by-step Introduction (Size: 2.9 GB)
  1. Bayes' Rule Introduction.mp4 63.4 MB
  1. Binomial Random Variables.mp4 101.4 MB
  1. Continuous Priors and the Uniform Distribution.mp4 67.3 MB
  1. Introduction Video.mp4 14.7 MB
  1. Likelihood Poisson Distribution.mp4 76.1 MB
  1. Normal Distribution.mp4 67.6 MB
  1. Predictive distributions, unknown variance.mp4 69.5 MB
  10. Predictive Distribution.mp4 62.2 MB
  10. Recap of Priors, Posteriors, Likelihoods, Predictive Distributions.mp4 37.2 MB
  11. Continuous Uniform Distribution Quiz.html 102.4 B
  11. Example Beta-Binomial Problem with R.mp4 105.8 MB
  11.1 BayesianBeta-BinomialRExercise.qmd 8.6 KB
  11.2 Python_BetaBinomial.ipynb 83.1 KB
  12. Example Beta-Binomial Problem with Stan.mp4 90.5 MB
  12.1 BayesianBeta-BinomialRExercise.qmd 8.6 KB
  12.2 Python_BetaBinomial.ipynb 83.1 KB
  13. Drawbacks of Conjugate Priors.mp4 33.6 MB
  2. Gamma Distribution and Choosing Hyperparameters.mp4 112.4 MB
  2. Likelihood and Random Variables.mp4 71.6 MB
  2. Medical Testing Problem.mp4 58.7 MB
  2. Prior Normal Distribution and choosing hyperparameters.mp4 83.9 MB
  2. Prior The Beta Distribution.mp4 86.9 MB
  2. Simple Linear Regression.mp4 82 MB
  2. Welcome.html 1.7 KB
  3. Example Simple Linear Regression Problem with R and Stan.mp4 83.2 MB
  3. Finding prior and posterior probabilities with the Beta CDF.mp4 20.4 MB
  3. Posterior Distribution One Observation.mp4 64.6 MB
  3. Posterior Distribution and Example.mp4 72.7 MB
  3. Posterior Distribution.mp4 64.6 MB
  3. Predictive Distributions and Flow Charts.mp4 40.5 MB
  3. Probability, Complements, Venn Diagrams.mp4 54.7 MB
  3.1 BayesianRegressionExample.qmd 3.6 KB
  3.1 Normal Posterior Distribution Full Derivation.html 102.4 B
  3.2 BayesianRegressionPython.ipynb 4.1 KB
  4. Credible Intervals and Point Estimates.mp4 25.8 MB
  4. How do the hyperparameters change the prior.mp4 27.4 MB
  4. Law of Total Probability.mp4 35.9 MB
  4. Posterior Calculation Example 2.mp4 21.9 MB
  4. Posterior Probability Calculation.mp4 23.2 MB
  4. Two Coins.mp4 54.4 MB
  5. Conditional Probability.mp4 63.6 MB
  5. Credible Interval and Point Estimates.mp4 31 MB
  5. Multiple Observations.mp4 46.9 MB
  5. Normal-Normal Quiz.html 102.4 B
  5. Posterior Distribution Two Observations.mp4 56.8 MB
  5. Posterior Distribution What are Conjugate Priors.mp4 118.4 MB
  6. CDFs and Inverse CDFs.mp4 91.7 MB
  6. Credible Intervals.mp4 49.3 MB
  6. Example Normal-Normal Problem with R and Stan.mp4 126.1 MB
  6. Multiplication Rule and Independence.mp4 61.7 MB
  6. Normalizing Constants and Proportionality.mp4 62.3 MB
  6. Predictive Distribution Negative Binomial.mp4 33.2 MB
  6.1 BayesianNormalRExample.qmd 6 KB
  6.2 Python-normalnormal.ipynb 7.9 KB
  7. Bayes Rule Quiz.html 102.4 B
  7. Credible Intervals.mp4 68.4 MB
  7. Point Estimates.mp4 37.3 MB
  7. Poisson - Gamma Quiz.html 102.4 B
  7. Probability Quiz.html 102.4 B
  8. Bayes' Rule Extra Practice Problems.html 102.4 B
  8. Example Poisson-Gamma Problem with R and Stan.mp4 108 MB
  8. Mean, Median, Mode.mp4 62 MB
  8. Predictive Distribution Beta-Binomial.mp4 43.4 MB
  8. Probability Extra Practice Problems.html 102.4 B
  8.1 Bayes_Rule_Practice.pdf 89.2 KB
  8.1 BayesianPoisson-GammaRexample.qmd 6 KB
  8.1 Bayesian_Homework_1.pdf 107 KB
  8.2 PoissonGammaPython.ipynb 9.8 KB
  9. Beta Distribution Quiz.html 102.4 B
  9. Point Estimates.mp4 80.3 MB
  Bonus Resources.txt 409.6 B
  Get Bonus Downloads Here.url 204.8 B
  ▲ 72 total files

Description


Bayesian Statistics: A Step-by-step Introduction
https://FreeCourseWeb.com

Published 8/2023
Created by Brian Greco
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 51 Lectures ( 5h 34m ) | Size: 3 GB

A former Google data scientist helps you master the basics of Bayesian statistics, with examples in R and Stan

What you'll learn
Understand how Bayes' rule can be used to update beliefs
Use conjugate priors and likelihoods to model binary, count, and continuous data
Understand the concepts of prior distributions, posterior distributions, likelihood functions, and predictive distributions
Understand how statistical software can be used to compute and visualize information about your beliefs

Requirements
Strong skills in basic algebra and arithmetic
Some knowledge of calculus is useful, but not required.

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