Our capacity to fathom the world around us hinges on our ability to understand quantities which are inherently unpredictable. Therefore, in order to gain more accurate mathematical models of the natural world we must incorporate probability into the mix. This course will serve as an introductions to the foundations of probability theory. Topics covered will include some of the following: (discrete and continuous)random variable, random vectors, multivariate distributions, expectations; independence, conditioning, conditional distributions and expectations; strong law of large numbers and the central limit theorem; random walks and Markov chains. There is an honors version of this course: seeMATH 60.
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| Name | Reviews |
|---|---|
| Carl Pomerance | 9 |
| Peter Winkler | 7 |
| Bohan Zhou, Martin Tassy | 6 |
| Emily B. | 6 |
| Marius Ionescu | 6 |
| James E. | 5 |
| Jan Glaubitz | 5 |
| Erik Slivken | 5 |
| John M. | 4 |
| Yixin Lin | 4 |
| Mark Skandera | 4 |
| Owen Dearricott | 3 |
| Sergi Elizalde | 3 |
| Misha Temkin | 2 |
| Bohan Zhou | 2 |
| Juan Auli Botero | 2 |
| Scott Pauls | 2 |
| Jeffrey A. | 2 |
| Geoff R. | 2 |
| Alina Glaubitz | 2 |
| Joseph R. | 1 |
| Rosa Orellana | 1 |
| Samuel Tripp | 1 |
| Nathan McNew | 1 |
| Amir Barghi | 1 |
| Geoffrey W. | 1 |
| Paul A. | 1 |
| Linh Huynh | 1 |
| Christopher Coscia | 0 |
| Edgar Martins Dias Costa | 0 |
| Feng Fu | 0 |
| Jay Pantone | 0 |
| Jiayi Chen | 0 |
| Laura Petto | 0 |
| Martin Tassy | 0 |
| Philip Hanlon | 0 |
| Seema Nanda | 0 |
| Xingru Chen | 0 |
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