6.041 / 6.431 Probabilistic Systems Analysis and Applied Probability, Fall 2002
Modeling, quantification, and analysis of uncertainty. 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. Interpretations, applications...
| Main Authors: | , , , |
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| Format: | Learning Object |
| Language: | en-US |
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2002
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| Online Access: | http://hdl.handle.net/1721.1/35860 |
| _version_ | 1826207014372507648 |
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| author | Medard, Muriel Tsitsiklis, John N. Bertsekas, Dimitri P. Abou Faycal, Ibrahim C. (Ibrahim Chafik) |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Medard, Muriel Tsitsiklis, John N. Bertsekas, Dimitri P. Abou Faycal, Ibrahim C. (Ibrahim Chafik) |
| author_sort | Medard, Muriel |
| collection | MIT |
| description | Modeling, quantification, and analysis of uncertainty. 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. Interpretations, applications, and lecture demonstrations. Meets with graduate subject 6.431, but assignments differ. From the course home page: Course Description 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. The materials are largely based on the textbook, Dynamic Programming and Optimal Control, written by Professors John Tsitsiklis and Dimitri Bertsekas (see http://www.athenasc.com/probbook.html for more information). |
| first_indexed | 2024-09-23T13:42:28Z |
| format | Learning Object |
| id | mit-1721.1/35860 |
| institution | Massachusetts Institute of Technology |
| language | en-US |
| last_indexed | 2025-03-10T11:38:46Z |
| publishDate | 2002 |
| record_format | dspace |
| spelling | mit-1721.1/358602025-02-25T16:58:24Z 6.041 / 6.431 Probabilistic Systems Analysis and Applied Probability, Fall 2002 Probabilistic Systems Analysis and Applied Probability Medard, Muriel Tsitsiklis, John N. Bertsekas, Dimitri P. Abou Faycal, Ibrahim C. (Ibrahim Chafik) Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science probabilistic systems probabilistic systems analysis applied probability uncertainty uncertainty modeling uncertainty quantification analysis of uncertainty uncertainty analysis sample space random variables transform techniques simple random processes probability distribution Markov process limit theorem statistical inference 6.041 6.431 Probabilities Modeling, quantification, and analysis of uncertainty. 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. Interpretations, applications, and lecture demonstrations. Meets with graduate subject 6.431, but assignments differ. From the course home page: Course Description 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. The materials are largely based on the textbook, Dynamic Programming and Optimal Control, written by Professors John Tsitsiklis and Dimitri Bertsekas (see http://www.athenasc.com/probbook.html for more information). 2002-12 Learning Object 6.041-Fall2002 local: 6.041 local: 6.431 local: IMSCP-MD5-5bad3c0f3b163a61a1938e3597dd4cb2 http://hdl.handle.net/1721.1/35860 en-US Usage Restrictions: This site (c) Massachusetts Institute of Technology 2003. 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"). 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. 16936 bytes 22268 bytes 36278 bytes 14623 bytes 19804 bytes 48244 bytes 14838 bytes 34552 bytes 13594 bytes 11 bytes 4586 bytes 21366 bytes 11602 bytes 38351 bytes 4755 bytes 27322 bytes 25313 bytes 4039 bytes 301 bytes 354 bytes 339 bytes 180 bytes 285 bytes 67 bytes 17685 bytes 49 bytes 143 bytes 247 bytes 19283 bytes 262 bytes 47405 bytes 536144 bytes 322804 bytes 176482 bytes 194648 bytes 67533 bytes 113005 bytes 214391 bytes 104810 bytes 322712 bytes 115454 bytes 83461 bytes 64349 bytes 271541 bytes 79166 bytes 403902 bytes 44676 bytes 179560 bytes 288281 bytes 355956 bytes 273429 bytes 241237 bytes 147409 bytes 78834 bytes 128777 bytes 121833 bytes 109498 bytes 43136 bytes 42327 bytes 45073 bytes 575257 bytes 51723 bytes 96311 bytes 48718 bytes 664062 bytes 69329 bytes 627602 bytes 58979 bytes 121006 bytes 43975 bytes 76328 bytes 865892 bytes 715029 bytes 51853 bytes 962622 bytes 56063 bytes 143681 bytes 48586 bytes 89499 bytes 61919 bytes 620659 bytes 67179 bytes 703243 bytes 67849 bytes 664159 bytes 42548 bytes 43561 bytes 47904 bytes 747599 bytes 57687 bytes 719553 bytes 75333 bytes 664549 bytes 68493 bytes 60540 bytes 55645 bytes 642253 bytes 64178 bytes 720914 bytes 58680 bytes 65588 bytes 58220 bytes 700141 bytes 64547 bytes 767696 bytes 53833 bytes 978834 bytes 83211 bytes 96188 bytes 94255 bytes 95915 bytes 91809 bytes 83009 bytes 130868 bytes 73583 bytes 103213 bytes 94298 bytes 124257 bytes 73096 bytes 119663 bytes 108060 bytes 111382 bytes 103540 bytes 121514 bytes 64671 bytes 285559 bytes 55654 bytes 163340 bytes 106108 bytes 152166 bytes 117455 bytes 115004 bytes 780878 bytes 620760 bytes 122057 bytes 216916 bytes 16816 bytes 92535 bytes 19283 bytes 3486 bytes 811 bytes 813 bytes 830 bytes 621 bytes 2097 bytes 134133 bytes 10426 bytes 10167 bytes 10439 bytes 10298 bytes 10377 bytes 10366 bytes 10457 bytes 10363 bytes 10432 bytes 10378 bytes 10357 bytes 10364 bytes 10377 bytes 10381 bytes 10364 bytes 10298 bytes 10402 bytes 10372 bytes 10356 bytes 10375 bytes 10410 bytes 10357 bytes 9757 bytes 10145 bytes 10368 bytes 10378 bytes 10372 bytes 10375 bytes 10398 bytes 9827 bytes 10376 bytes 10333 bytes 10389 bytes 10366 bytes 10356 bytes 10363 bytes 10363 bytes 10440 bytes 10367 bytes 9883 bytes 10376 bytes 10357 bytes 10363 bytes 10391 bytes 10382 bytes 10386 bytes 10356 bytes 10386 bytes 9685 bytes 10378 bytes 10363 bytes 10379 bytes 10376 bytes 10363 bytes 10364 bytes 10364 bytes 10356 bytes 10368 bytes 10368 bytes 10334 bytes 10368 bytes 10390 bytes 10475 bytes 10374 bytes 9891 bytes 10381 bytes 10377 bytes 10428 bytes 10364 bytes 10412 bytes 10377 bytes 9849 bytes 10375 bytes 9787 bytes 10397 bytes 10366 bytes 9701 bytes 10360 bytes 10405 bytes 10439 bytes 10378 bytes 10976 bytes 10383 bytes 10414 bytes 10399 bytes 10361 bytes 10348 bytes 10458 bytes 10433 bytes 10385 bytes 10357 bytes 10376 bytes 10375 bytes 10343 bytes 10366 bytes 10445 bytes 10365 bytes 10324 bytes 10296 bytes 10368 bytes 10363 bytes 10357 bytes 10378 bytes 10387 bytes 10356 bytes 10377 bytes 10442 bytes 10377 bytes 10364 bytes 10387 bytes 10315 bytes 10356 bytes 10367 bytes 10297 bytes 10377 bytes 10355 bytes 10374 bytes text/html Fall 2002 |
| spellingShingle | probabilistic systems probabilistic systems analysis applied probability uncertainty uncertainty modeling uncertainty quantification analysis of uncertainty uncertainty analysis sample space random variables transform techniques simple random processes probability distribution Markov process limit theorem statistical inference 6.041 6.431 Probabilities Medard, Muriel Tsitsiklis, John N. Bertsekas, Dimitri P. Abou Faycal, Ibrahim C. (Ibrahim Chafik) 6.041 / 6.431 Probabilistic Systems Analysis and Applied Probability, Fall 2002 |
| title | 6.041 / 6.431 Probabilistic Systems Analysis and Applied Probability, Fall 2002 |
| title_full | 6.041 / 6.431 Probabilistic Systems Analysis and Applied Probability, Fall 2002 |
| title_fullStr | 6.041 / 6.431 Probabilistic Systems Analysis and Applied Probability, Fall 2002 |
| title_full_unstemmed | 6.041 / 6.431 Probabilistic Systems Analysis and Applied Probability, Fall 2002 |
| title_short | 6.041 / 6.431 Probabilistic Systems Analysis and Applied Probability, Fall 2002 |
| title_sort | 6 041 6 431 probabilistic systems analysis and applied probability fall 2002 |
| topic | probabilistic systems probabilistic systems analysis applied probability uncertainty uncertainty modeling uncertainty quantification analysis of uncertainty uncertainty analysis sample space random variables transform techniques simple random processes probability distribution Markov process limit theorem statistical inference 6.041 6.431 Probabilities |
| url | http://hdl.handle.net/1721.1/35860 |
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