STAT 110: Introduction to Probability
|Course Head||Joe Blitzstein|
|Last run||Fall 2019|
|Q-Guide Overall||4.2 (Fall 2019)|
|Q-Guide Workload (hrs/week)||9.8 (Fall 2018)|
|Enrollment||544 (Fall 2018)|
A comprehensive introduction to probability, as a language and set of tools for understanding statistics, science, risk, and randomness. Basics: sample spaces and events, conditional probability, and Bayes’ Theorem. Univariate distributions: density functions, expectation and variance, Normal, t, Binomial, Negative Binomial, Poisson, Beta, and Gamma distributions. Multivariate distributions: joint and conditional distributions, independence, transformations, and Multivariate Normal. Limit laws: law of large numbers, central limit theorem. Markov chains: transition probabilities, stationary distributions, convergence.
The world is replete with randomness and uncertainty; probability and statistics extend logic into this realm. We will systematically introduce the ideas and tools of probability, which are useful in statistics, science, philosophy, engineering, economics, finance, and everyday life. Both the mathematical results of the subject and applications to solving problems will be studied, with examples ranging from gambling to genetics.
Even Shorter Description
How to understand and work with randomness and uncertainty through probability models, random variables and their distributions, and thinking conditionally.
Assignments and Exams
The weighting of assignments and exams is as follows:
- Problem Sets: 35%
- Midterm: 10-25%
- Final: 40-55%