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Python for Data Science

John Stachurski

Changelog (full history)

5f9aa91 John Stachurski 1 minute ago Drop the reference to an earlier version's imputation bug

13. Markov Chains and Social Mobility#

Note

This lecture is under construction.

Topics:

Transition matrices and distributions over time
Stationary distributions
Why the law of large numbers can still hold without independence

Creative Commons License – This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International.

A theme by QuantEcon

Contents

Getting Started

1. Introduction to the Course
2. Reading Python and Directing AI
3. The Toolkit: What Is Possible
4. Working with Data

Distributions and Estimation

5. Probability Distributions
6. Observed Distributions and Matching Them to Data
7. Bivariate Distributions, Correlation and Simple Regression
8. Maximum Likelihood Estimation
9. Bayesian Estimation and Uncertainty

Prediction

10. Prediction
11. Classification, Clustering and Forecasting

Inequality, Mobility and Networks

12. Heavy Tails and Inequality
13. Markov Chains and Social Mobility
14. Networks
15. Application: A Wealth Tax

Other

References

QuantEcon

Lecture (PDF)
Book (PDF)

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