Probability, Random Processes, and Statistical Thinking

Classifications / Mathematics And Formal Foundations / Probability, Random Processes, and Statistical Thinking

TRACK

Build a practical probability model of uncertain events, samples, measurements, and processes, then test what its summaries can and cannot justify.

How do I reason about uncertainty by modeling events, random variables, distributions, dependence, sampling, noise, stochastic processes, and statistical evidence without mistaking randomness for ignorance?

16 lessons

Continues to Decision Making, Uncertainty, and Judgment/ Information Theory, Coding, and Compression/ Large-Scale Data Mining/ Probabilistic Modeling and Bayesian Inference/ Reinforcement Learning and Sequential Decision Making/ Scientific Reasoning and Philosophy of Science

LESSONS

1. 001
   Probability Begins by Naming What Could Happen
   [TODO]
2. 002
   Random Variables Turn Outcomes into Distributions
   [TODO]
3. 003
   Expectation Is a Summary, Not a Promise
   [TODO]
4. 004
   Dependence Changes the Meaning of Evidence
   [TODO]
5. 005
   Simulation Lets Randomness Move
   [TODO]
6. 006
   Review: From Sample Spaces to Conditional Models REVIEW
   [TODO]
7. 007
   Samples Change Even When the Process Does Not
   [TODO]
8. 008
   Intervals Are More Honest Than Point Estimates
   [TODO]
9. 009
   Statistical Evidence Needs a Comparison
   [TODO]
10. 010
    Heavy Tails Make the Average Fragile
    [TODO]
11. 011
    Measurement Noise Is Not Process Variation
    [TODO]
12. 012
    Random Processes Put Uncertainty in Motion
    [TODO]
13. 013
    Hidden State Gives the Past a Memory
    [TODO]
14. 014
    Queues, Bursts, and Locality Create Dependence
    [TODO]
15. 015
    Review: Stress-Test an Uncertainty Model REVIEW
    [TODO]
16. 016
    Capstone: Diagnose a Noisy System CAPSTONE
    [TODO]

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