Data

Predicting with Probability

Learn to make predictions under uncertainty using Bayesian methods and decision theory. From prior beliefs to time series forecasting, master probabilistic prediction.

1

Frequentist vs Bayesian

Two philosophies of probability

Lesson 1: Frequentist Interpretation Coming Soon
Lesson 2: Bayesian Interpretation Coming Soon
Lesson 3: When to Use Each Coming Soon
2

Prior & Posterior

Beliefs before and after evidence

Lesson 4: Choosing Priors Coming Soon
Lesson 5: Computing Posteriors Coming Soon
Lesson 6: Conjugate Priors Coming Soon
3

Likelihood

How probable is the evidence?

Lesson 7: Likelihood Function Coming Soon
Lesson 8: Maximum Likelihood Estimation Coming Soon
Lesson 9: Likelihood Ratio Coming Soon
4

Bayesian Updating

Refining predictions with new data

Lesson 10: Sequential Updating Coming Soon
Lesson 11: Beta-Binomial Model Coming Soon
Lesson 12: Bayesian Learning Coming Soon
5

Predictive Models

Forecasting with probability

Lesson 13: Predictive Distributions Coming Soon
Lesson 14: Posterior Predictive Checks Coming Soon
Lesson 15: Model Comparison Coming Soon
6

Decision Theory

Making optimal choices under uncertainty

Lesson 16: Expected Utility Coming Soon
Lesson 17: Loss Functions Coming Soon
Lesson 18: Decision Rules Coming Soon
7

Risk Assessment

Quantifying and managing risk

Lesson 19: Probability of Rare Events Coming Soon
Lesson 20: Risk vs Uncertainty Coming Soon
Lesson 21: Value at Risk Coming Soon
8

Time Series

Predicting sequential data

Lesson 22: Trend & Seasonality Coming Soon
Lesson 23: Autoregression Coming Soon
Lesson 24: Moving Averages Coming Soon
9

Real-World Forecasting

Applied prediction in practice

Lesson 25: Weather Forecasting Coming Soon
Lesson 26: Financial Prediction Coming Soon
Lesson 27: Election Forecasting Coming Soon