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The theory and applications of inference, hypothesis testing, estimation, random walks, large deviations, martingales and investments are developed. Written by one of the world's leading information theorists, evolving over twenty years of graduate classroom teaching and enriched by over exercises, this is an exceptional resource for anyone looking to develop their understanding of stochastic processes.

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SEMINARS AND INVITED LECTURES - Enzo Orsingher

Do you have technical problems? Write to us: coursera hse. Upon completing this week, the learner will be able to understand the basic notions of probability theory, give a definition of a stochastic process; plot a trajectory and find finite-dimensional distributions for simple stochastic processes. Moreover, the learner will be able to apply Renewal Theory to marketing, both calculate the mathematical expectation of a countable process for any renewal process. Upon completing this week, the learner will be able to understand the definitions and main properties of Poisson processes of different types and apply these processes to various real-life tasks, for instance, to model customer activity in marketing and to model aggregated claim sizes in insurance; understand a relation of this kind of models to Queueing Theory.

Upon completing this week, the learner will be able to identify whether the process is a Markov chain and characterize it; classify the states of a Markov chain and apply ergodic theorem for finding limiting distributions on states. Upon completing this week, the learner will be able to understand the notions of Gaussian vector, Gaussian process and Brownian motion Wiener process ; define a Gaussian process by its mean and covariance function and apply the theoretical properties of Brownian motion for solving various tasks.

Upon completing this week, the learner will be able to determine whether a given stochastic process is stationary and ergodic; determine whether a given stochastic process has a continuous modification; calculate the spectral density of a given wide-sense stationary process and apply spectral functions to the analysis of linear filters. Upon completing this week, the learner will be able to determine whether a given stochastic process is differentiable and apply the term of continuity and ergodicity to stochastic processes. This was helpful but I still feel I don't understand stochastic processes.

Folks taking this course should know that it's pretty tough, compared to most Coursera courses. Great course! The subject material was well covered and it gave me the tools to tackle more advanced stochastic, like population dynamics or quantitative finance.

Mod-01 Lec-06 Stochastic processes

Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments. When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

If you only want to read and view the course content, you can audit the course for free. More questions? Visit the Learner Help Center. Browse Chevron Right. Math and Logic Chevron Right. Math and Logic. Stochastic processes.

SS 19: Theory of Stochastic Processes

Offered By. About this Course 35, recent views. Flexible deadlines. Flexible deadlines Reset deadlines in accordance to your schedule. Intermediate Level. Hours to complete. Available languages. English Subtitles: English. Chevron Left. Syllabus - What you will learn from this course.