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443415.070J Advanced Stochastic Processes (MIT)
http://www.merlot.org/merlot/viewMaterial.htm?id=884047
This class covers the analysis and modeling of stochastic processes. Topics include measure theoretic probability, martingales, filtration, and stopping theorems, elements of large deviations theory, Brownian motion and reflected Brownian motion, stochastic integration and Ito calculus and functional limit theorems. In addition, the class will go over some applications to finance theory, insurance, queueing and inventory models.18.440 Probability and Random Variables (MIT)
http://www.merlot.org/merlot/viewMaterial.htm?id=884379
This course introduces students to probability and random variables. Topics include distribution functions, binomial, geometric, hypergeometric, and Poisson distributions. The other topics covered are uniform, exponential, normal, gamma and beta distributions; conditional probability; Bayes theorem; joint distributions; Chebyshev inequality; law of large numbers; and central limit theorem.6.262 Discrete Stochastic Processes (MIT)
http://www.merlot.org/merlot/viewMaterial.htm?id=884733
Discrete stochastic processes are essentially probabilistic systems that evolve in time via random changes occurring at discrete fixed or random intervals. This course aims to help students acquire both the mathematical principles and the intuition necessary to create, analyze, and understand insightful models for a broad range of these processes. The range of areas for which discrete stochastic-process models are useful is constantly expanding, and includes many applications in engineering, physics, biology, operations research and finance.17.871 Political Science Laboratory (MIT)
http://www.merlot.org/merlot/viewMaterial.htm?id=883732
This course introduces students to the conduct of political research using quantitative methodologies. The methods are examined in the context of specific political research activities like public opinion surveys, voting behavior, Congressional behavior, comparisons of political processes in different countries, and the evaluation of public policies. Students participate in joint class projects and conduct individual projects.RES.14-001 Abdul Latif Jameel Poverty Action Lab Executive Training: Evaluating Social Programs 2009 (MIT)
http://www.merlot.org/merlot/viewMaterial.htm?id=884265
This five-day program on evaluating social programs will provide a thorough understanding of randomized evaluations and pragmatic step-by-step training for conducting one's own evaluation. While the course focuses on randomized evaluations, many of the topics, such as measuring outcomes and dealing with threats to the validity of an evaluation, are relevant for other methodologies. About the Abdul Latif Jameel Poverty Action Lab J-PAL's goal is to reduce poverty by ensuring that policy is based on scientific evidence. Every day, evidence generated by J-PAL researchers is influencing policy and improving lives, sometimes very directly – for example through the scale-up of effective programs – but also in less direct but equally important ways. To date, our evidence has helped improve the lives of at least 30 million people around the world through the scale-up of highly effective policies and programs. By 2013, J-PAL aims to have positively impacted 100 million lives.6.041SC Probabilistic Systems Analysis and Applied Probability (MIT)
http://www.merlot.org/merlot/viewMaterial.htm?id=884026
This course introduces students to the modeling, quantification, and analysis of uncertainty. The tools of probability theory, and of the related field of statistical inference, are the keys for being able to analyze and make sense of data. These tools underlie important advances in many fields, from the basic sciences to engineering and management.6.041 Probabilistic Systems Analysis and Applied Probability (MIT)
http://www.merlot.org/merlot/viewMaterial.htm?id=884165
Welcome to 6.041/6.431, a subject on the modeling and analysis of random phenomena and processes, including the basics of statistical inference. Nowadays, there is broad consensus that the ability to think probabilistically is a fundamental component of scientific literacy. For example: The concept of statistical significance (to be touched upon at the end of this course) is considered by the Financial Times as one of "The Ten Things Everyone Should Know About Science". A recent Scientific American article argues that statistical literacy is crucial in making health-related decisions. Finally, an article in the New York Times identifies statistical data analysis as an upcoming profession, valuable everywhere, from Google and Netflix to the Office of Management and Budget. The aim of this class is to introduce the relevant models, skills, and tools, by combining mathematics with conceptual understanding and intuition.6.042J Mathematics for Computer Science (MIT)
http://www.merlot.org/merlot/viewMaterial.htm?id=884148
This course covers elementary discrete mathematics for computer science and engineering. It emphasizes mathematical definitions and proofs as well as applicable methods. Topics include formal logic notation, proof methods; induction, well-ordering; sets, relations; elementary graph theory; integer congruences; asymptotic notation and growth of functions; permutations and combinations, counting principles; discrete probability. Further selected topics may also be covered, such as recursive definition and structural induction; state machines and invariants; recurrences; generating functions.6.042J Mathematics for Computer Science (MIT)
http://www.merlot.org/merlot/viewMaterial.htm?id=884196
This subject offers an introduction to Discrete Mathematics oriented toward Computer Science and Engineering. The subject coverage divides roughly into thirds: Fundamental concepts of mathematics: definitions, proofs, sets, functions, relations. Discrete structures: graphs, state machines, modular arithmetic, counting. Discrete probability theory. On completion of 6.042, students will be able to explain and apply the basic methods of discrete (noncontinuous) mathematics in Computer Science. They will be able to use these methods in subsequent courses in the design and analysis of algorithms, computability theory, software engineering, and computer systems.8.044 Statistical Physics I (MIT)
http://www.merlot.org/merlot/viewMaterial.htm?id=884608
This course offers an introduction to probability, statistical mechanics, and thermodynamics. Numerous examples are used to illustrate a wide variety of physical phenomena such as magnetism, polyatomic gases, thermal radiation, electrons in solids, and noise in electronic devices.