教材分析 MIT机电工程与计算机科学系【本科生课程】6.041.随机系统分析与应用概率.Probabilistic.Systems.Analysis.and.Applied.Probability[www.ed2kers.com].pdf

Probabilistic Systems Analysis and Applied Probability As taught in: Spring 2006 Why flip a coin? Try these instead to increase the number of outcomes. (Image courtesy of Idiolector .) Instructors: Prof. Munther Dahleh MIT Course Number: 6.041 / 6.431 Level: Undergraduate / Graduate 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. Syllabus This section contains detailed information about the course components. A list of topics covered in the course is available in the calendar section. Course Meeting Times Lectures: 2 sessions / week, 1 hour / session Recitations: 2 sessions / week, 1 hour / session Tutorials: 1 session / week, 1 hour / session Prerequisites The prerequisite for 6.041 and 6.431 is 18.02, or a year of college level calculus for those with undergraduate degrees from other universities. Students who have not completed the prerequisite with a grade of A, B, C or P may not enroll. 6.041 and 6.431 Students intending to take the undergraduate version of the course need to sign up for 6.041, while those intending to take the graduate version should sign up for 6.431, which includes full participation in 6.041, together with some additional homework problems, additional topics, and possibly different quiz and exam questions. 6.041/6.431 has three types of class sessions: lectures, recitations, and tutorials. The lectures and recitations each meet twice a week. In addition, there will be a tutorial once a week, which is not mandato