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Markov Chain Monte Carlo
| Content Provider | Scilit |
|---|---|
| Author | Blitzstein, Joseph K. Hwang, Jessica |
| Copyright Year | 2014 |
| Description | We have seen throughout this book that simulation is a powerful technique in probability. If you can't convince your friend that it is a good idea to switch doors in the Monty Hall problem, in one second you can simulate playing the game a few thousand times and your friend will just see that switching succeeds about 2/3 of the time. If you're unsure how to calculate the mean and variance of an r.v. X but you know how to generate i.i.d. draws X1, X2, . . . , Xn from that distribution, you can approximate the true mean and true variance using the sample mean and sample variance of the simulated draws: E(X) ≈ 1 n (X1 + · · ·+Xn) = X¯n, Var(X) ≈ 1 n− 1 (Xj − X¯n)2. Book Name: Introduction to Probability |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2012-0-08408-3&isbn=9780429102103&doi=10.1201/b17221-14&format=pdf |
| Ending Page | 535 |
| Page Count | 24 |
| Starting Page | 512 |
| DOI | 10.1201/b17221-14 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2014-07-24 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Introduction to Probability Statistics and Probability |
| Content Type | Text |
| Resource Type | Chapter |