[Table of Contents] Experiment! Bayesian Modeling from Iwanami Data Science 1 with PyMC Ver. 5

This article is the table of contents for the "Experiment! Bayesian Modeling from Iwanami Data Science 1 with PyMC Ver. 5" series.

Introduction to "Iwanami Data Science Vol. 1"

The text "Iwanami Data Science Vol. 1," released in October 2015, is a wonderful book that introduces the fun of Bayesian modeling by presenting numerous modeling examples using various software.

Table of Contents / Links

Python MCMC Library: PyMC

① Linear Regression Model
📈 Let's learn the basics of Bayesian modeling with $${y=a+bx}$$!

② Hierarchical Bayesian Model
📈 Plant species and mysterious binomial distribution models lead to the "Green Book"

③ Sampling Discrete Variables
📈 Change-point detection model using a dataset practiced by the official PyMC website

④ Stock Price Analysis
📈 Visualize stock index volatility with secret code

The First Step into Hierarchical Bayes

⑤ Straight Line Fitting
📈 Did the change in school lunch affect height growth!? [Part 1] Linear regression models are...

⑥ Hierarchical Bayesian Model
📈 Did the change in school lunch affect height growth!? [Part 2] Solved with hierarchical Bayes!

Modeling Time-Series and Spatial Data

⑦ State-Space Model: Japanese Cedar Tree Ring Width Data
📈 Local level models, trend models, log-normal distribution models, and Japanese cedar

⑧ State-Space Model: Wild Bird Count Data
📈 Find hidden birds with state-space models, binomial distributions, and Poisson distributions!

⑨ Spatial Autoregressive Model: 1D Population Count Data
📈 Spatial autoregressive models with CAR (not the vehicle), and on to the "Green Book"

⑩ Spatial Autoregressive Model: 2D Tree Count Data
📈 Visualize an acorn forest with a 2D ICAR model, the first step toward forest conservation

Introduction to Stan (Note: No practical Stan exercises)

⑪ Bayesian Version of Least Squares Method
📈 It starts (as expected) with a linear regression model, focusing on unknown predictions

⑫ State-Space Models and Bayesian Decision Making
📈 Predict the future from time-series sales performance, from business decision-making to ecosystems

⑬ Bayesian models with spatial structure
📈 The ICAR model with second-order differences, the final installment is always full of drama

Blog Introduction

I am writing eight series of articles on note.
Please come and take a look!

1. Relaxed Statistics

This is a blog that roughly explores probability and statistics using the Statistical Test Grade 2 problem collection as a guide.
Feel free to read it like casual conversation. Please come and take a look.
It corresponds to the CBT-compatible version of the official Statistical Test Grade 2 problem collection.
Sample code for Python and EXCEL is also available.

2. Connecting with Statistics and Data Analysis

The series "Connecting with Statistics and Data Analysis" is a blog series written in a column style that uncovers "connections" to statistics and data analysis.
With the help of generative AI, I wrote these freely, imagining the entry points to statistics and data analysis.
For example...
・Connections between daily life and statistics
・Connections from Statistical Test Grade 2 to what lies beyond
I hope you enjoy reading them casually 🍀

3. Experiment! Fun Bayesian Modeling 1 & 2 with PyMC Ver. 5

I will draw and analyze the Bayesian models used in psychological research from the books "Fun Bayesian Modeling" and "Fun Bayesian Modeling 2" using PyMC Ver. 5.
Like this book, many Bayesian models are written in R + Stan.
I strive to explore the possibilities of PyMC and make Bayesian modeling easy to practice.
Since these are familiar and easy-to-visualize themes, please try running them with PyMC and let's enjoy it together!

4. Experiment! Bayesian Modeling from Iwanami Data Science 1 with PyMC Ver. 5

I will draw and analyze the Bayesian models by four Bayesians from the book "Experiment! Iwanami Data Science Vol. 1" using PyMC Ver. 5.
This book is a great resource for learning the basics of Bayesian programming.
I feel like I've become friends with Bayesian methods by happily running PyMC models.
Everyone, please try running them with PyMC and let's play and learn together!

5. Fun Transcription: Bayesian, Python, etc.

I will blog about the results of my "book transcription activities" for Bayesian, Python, and others.
I am mainly working on translations into Python.
I hope this becomes useful sample code for fellow transcribers 🍀

6. Introduction to Time Series Analysis for Psychology with R and Stan, using Python and PyMC Ver. 5

I will practice the time series analysis from the book "Introduction to Time Series Analysis for Psychology with R and Stan" using Python and PyMC Ver. 5.
This book is packed with time series analysis themes!
I realized the depth of time series analysis.
I will enjoy learning time series analysis with my favorite language, Python.

7. Writing about Data Science-like things

I write columns on statistics, data analysis, AI, machine learning, and Python on an irregular basis.
There are many articles related to statistics and data science books.
Series on "Statistics," "Python," "Mathematics and Python," and "R" have been born.

8. Python Machine Learning Programming Practice Journal

I wrote articles about my various thoughts when studying the book "Python Machine Learning Programming: PyTorch & scikit-learn Edition."
This book is a textbook for scikit-learn and PyTorch.
Please feel free to give it a try.

Thank you for reading until the end.