Environment Installation with Anaconda

Environment Installation with Anaconda

Environment Installation with Anaconda

You'll have the best experience in this course with a local environment installed. In this section we provide step by step instructions for how to do this on your computer

Advanced Regression

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About this Course

Welcome to Advanced Regression

The Problem with Ordinary Regression

Bayesian Generalized Linear Models

What's Different Now

Who This Course is For

Outline and Prequisites

Course Presurvey

Lesson Feedback

Meet your Instructors

Resource Library

Intuitive Bayes Discourse Community

Github Repository and Code Access

Environment Installation with Anaconda

Optional Orientation: Discourse

Optional Orientation: Github

Optional Orientation: Podia

Regression Refresher

Introduction

Exploratory Data Analysis

Making a Plan

The World's Simplest Model

Adding a Slope

Transformations

Accounting for Species

When We Catch New Fish

From Predictions to Insights

Bayesian Workflow and Growing Pains

Lesson Recap

Lesson Feedback

Lesson References

Lesson Exercises

Bambi Deep Dive

Introduction

The World's Simplest Model, Now Simpler

Peeking Under The Hood

Sloping Up

Transformations in Bambi

Modeling Categories

Parameter Identifiability

Understanding Encodings

The Full Model

Predictions

An End to End Trip with Bambi

Lesson Recap

Lesson Feedback

Lesson Exercises

Lesson References

Advanced AB Testing and Link Functions

Introduction

Basic AB Test

AB Testing with Continuous Covariates

Link Functions and Generalized Linear Models

AB Testing End to End

United States Election Forecasting

Lesson Recap

Lesson Feedback Form

Lesson Exercises

Lesson References

Categorical Regression

Introduction

Nerdy crêpes

Our First Categorical model

Killing Me Softmax

Running our Categorical regression

Adding Regressors

Fun with Shapes

Unpleasant Probabilities

Sampling & Diagnosing

Debugging our model

Categorical model with Bambi

Introducing pm.ZeroSumNormal

SALK EDA

Coding the Salk model in PyMC

Sampling the Salk model

Estonia, Bambi styyyyyle!

Lesson Recap

Lesson Exercises

Lesson References

Lesson Feedback

Multinominal Regression

Introduction

Aggregating the Estonian data

Introducing geographical variables

Writing the Multinomial model

Coding the Model

Sampling & Posterior analysis

Taking all effects into account

Posterior Schematics

Taking sample size into account

Defining & Sampling the Bambi Multinomial model

Analyzing the posterior effects

Computing the latent probabilities

Comparing PyMC and Bambi estimates

Posterior Retrodictive Sampling

Lesson Recap

Lesson Exercises

Lesson References

Lesson Feedback

Counting Things

Introduction

Counting Things

Counting Regression

Poisson Distributions in Sports

Soccer Statistics

Home Field Advantage

Zero Inflated Poisson

Zero Inflated Fish

Lesson Recap

Lesson Exercises

Lesson Feedback

Lesson References

Overdispersed Regression

12 segundos de oscuridad

Unanticipated consequences

What's the deal with overdispersion

When the whole is more than the sum of the parts

Negative binomial regression

Overdispersion for all

Beta-binomial to the rescue

The beta-binomial model in action

Counting grasshoppers

Beta-binomial for grasshoppers

Lesson Recap

Lesson Feedback

Lesson References

Lesson Exercises

Hierarchical Models

Introduction

Pulling and Unpooling Extremities

Simple Hierarchical Model

Regularization to the mean

Complete Hierarchical Model

Non-centered hierarchical model

Sampling & Posterior Analysis

In-sample Predictions

Analysis of Variance

LOO comparison

Predicting on new groups

Estonian Hierarchy

Writing down the Hierarchical Model

Coding the Full Model

Hierarchical Multinomial, Deluxe Version

Sampling, Posterior & Post-stratification

Post-stratification in practice

Making post-stratified predictions

Visualizing Strata Predictions

A Deeper Cut

Lesson Recap

Lesson Feedback

Lesson References

Lesson Exercises

Final Notes

Congratulations