| dc.contributor.author | Bertsekas, Dimitri | en_US |
| dc.contributor.author | Tsitsiklis, John | en_US |
| dc.contributor.author | Médard, Muriel | en_US |
| dc.coverage.temporal | Spring 2005 | en_US |
| dc.date.issued | 2005-06 | |
| dc.identifier | 6.041-Spring2005 | |
| dc.identifier | local: 6.041 | |
| dc.identifier | local: 6.431 | |
| dc.identifier | local: IMSCP-MD5-844c8b4a58247e68aef30137b0b67e14 | |
| dc.identifier.uri | http://hdl.handle.net/1721.1/70858 | |
| dc.description.abstract | This course is offered both to undergraduates (6.041) and graduates (6.431), but the assignments differ. 6.041/6.431 introduces students to the modeling, quantification, and analysis of uncertainty. Topics covered include: formulation and solution in sample space, random variables, transform techniques, simple random processes and their probability distributions, Markov processes, limit theorems, and elements of statistical inference. | en_US |
| dc.language | en-US | en_US |
| dc.relation | | en_US |
| dc.rights.uri | Usage Restrictions: This site (c) Massachusetts Institute of Technology 2012. Content within individual courses is (c) by the individual authors unless otherwise noted. The Massachusetts Institute of Technology is providing this Work (as defined below) under the terms of this Creative Commons public license ("CCPL" or "license") unless otherwise noted. The Work is protected by copyright and/or other applicable law. Any use of the work other than as authorized under this license is prohibited. By exercising any of the rights to the Work provided here, You (as defined below) accept and agree to be bound by the terms of this license. The Licensor, the Massachusetts Institute of Technology, grants You the rights contained here in consideration of Your acceptance of such terms and conditions. | en_US |
| dc.subject | probabilistic systems | en_US |
| dc.subject | probabilistic systems analysis | en_US |
| dc.subject | applied probability | en_US |
| dc.subject | uncertainty | en_US |
| dc.subject | uncertainty modeling | en_US |
| dc.subject | uncertainty quantification | en_US |
| dc.subject | analysis of uncertainty | en_US |
| dc.subject | uncertainty analysis | en_US |
| dc.subject | sample space | en_US |
| dc.subject | random variables | en_US |
| dc.subject | transform techniques | en_US |
| dc.subject | simple random processes | en_US |
| dc.subject | probability distribution | en_US |
| dc.subject | Markov process | en_US |
| dc.subject | limit theorem | en_US |
| dc.subject | statistical inference | en_US |
| dc.subject | 6.041 | en_US |
| dc.subject | 6.431 | en_US |
| dc.title | 6.041 / 6.431 Probabilistic Systems Analysis and Applied Probability, Spring 2005 | en_US |
| dc.title.alternative | Probabilistic Systems Analysis and Applied Probability | en_US |
| dc.type | Learning Object | |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
