An introduction to probability, emphasizing the combined use of mathematics and programming. Discrete and continuous families of distributions. Bounds and approximations. Transforms and convergence. Markov chains and Markov Chain Monte Carlo. Dependence, conditioning, Bayesian methods. Maximum likelihood, least squares prediction, the multivariate normal, and multiple regression. Random permutations, symmetry, and order statistics. Use of numerical computation, graphics, simulation, and computer algebra.
UC Berkeley Data 140
UC Berkeley Probability for Data Science Course
Pinned Loading
Repositories
Showing 10 of 22 repositories
- materials-sp24 Public
prob140/materials-sp24’s past year of commit activity - materials-fa24 Public
prob140/materials-fa24’s past year of commit activity - materials-sp25 Public
prob140/materials-sp25’s past year of commit activity - materials-sp19 Public
prob140/materials-sp19’s past year of commit activity - materials-fa19 Public
prob140/materials-fa19’s past year of commit activity - materials-sp20 Public
prob140/materials-sp20’s past year of commit activity - materials-fa20 Public
prob140/materials-fa20’s past year of commit activity - materials-sp21 Public
prob140/materials-sp21’s past year of commit activity
People
This organization has no public members. You must be a member to see who’s a part of this organization.
Top languages
Loading…
Most used topics
Loading…