Вернуться к Stochastic processes

4.3

Оценки: 49

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Рецензии: 20

The purpose of this course is to equip students with theoretical knowledge and practical skills, which are necessary for the analysis of stochastic dynamical systems in economics, engineering and other fields.
More precisely, the objectives are
1. study of the basic concepts of the theory of stochastic processes;
2. introduction of the most important types of stochastic processes;
3. study of various properties and characteristics of processes;
4. study of the methods for describing and analyzing complex stochastic models.
Practical skills, acquired during the study process:
1. understanding the most important types of stochastic processes (Poisson, Markov, Gaussian, Wiener processes and others) and ability of finding the most appropriate process for modelling in particular situations arising in economics, engineering and other fields;
2. understanding the notions of ergodicity, stationarity, stochastic integration; application of these terms in context of financial mathematics;
It is assumed that the students are familiar with the basics of probability theory. Knowledge of the basics of mathematical statistics is not required, but it simplifies the understanding of this course.
The course provides a necessary theoretical basis for studying other courses in stochastics, such as financial mathematics, quantitative finance, stochastic modeling and the theory of jump - type processes....

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Рецензии: 21

автор: 贺瑞麟

•Dec 14, 2018

tres bon

автор: CHEN NI

•Dec 08, 2018

Very nice course :) But it takes time to be adopted to the teaching style...

автор: Zororo Makumbe

•Dec 01, 2018

Well presented course. I enjoyed it and was challenged a great deal. Thank you.

автор: 韩

•Nov 27, 2018

Please give us the explanation of the exercises, because I don't understand why my answer is wrong.

автор: Paul Karitis

•Nov 22, 2018

This is an excellent course for learning the theory of stochastic processes. I have taken many courses with Coursera and to me this ranks as one of their best. It is a difficult course and covers a lot of material but I found it to be a very well developed and well-presented course. After completion I feel that I significantly improved both my knowledge and understanding of stochastic processes and probability theory.

The professor is very engaging in the lectures and employs a classical style of teaching theoretical mathematics. Theorem, proof explanation, example, theorem, proof, explanation, example pretty much right from his head to the clear board. No software is needed though I found it helpful to simulate some processes to see how they worked or to do equation solving but it is not required. Although English is not the professor’s first language this was not a problem for me although it did cause some problems for the speech to text converter that Coursera uses. It didn’t matter too much as in my opinion the professor has excellent teaching skills and provides lucid explanations to some very difficult concepts. A few times there might be some elements of a proof left out due to rushing or mistakes made in them but this was not very often and they often got sorted out in the discussion sections. Still this is a course that puts a lot of demands on the student regardless of how perfect a lecture may be. It is just the nature of the subject material. It is the closest thing to a graduate course I have taken at Coursera and it should be considered as an advanced course rather than intermediate. Some background in probability and measure theory would be very helpful to a student as the professor makes use of it during the lectures. Some background on it is provided but I found I had to do some investigation of it on my own even having studied it some in the past.

The course was a bit short on resources so I included some links to web sites and book pdf's that I found useful which can be found in the Week One Discussion Forum.

Some things I noted: The quizzes are often learning exercises not simply a recitation of facts learned in the lectures. I found the threads in the discussion forums for each week very useful as they contain some history of discussions of various problems in the course.

I highly recommend this course to any students who are seeking to improve their understanding of stochastic processes and probability theory or for strengthening their understanding of other fields of statistics that are based on time dependence like time series analysis.

автор: 夏旸

•Nov 18, 2018

The content is very sufficient but if the class hour could be extended to about 30-50% more, it will help learners like me concretely understand the content within the class without referring to much more information and notes from other universities. By the way, it's good to learn things like Ito formula and Levy process in this class. Thank for the efforts made by Prof. Panov.

автор: Dmitry

•Nov 13, 2018

Nice course but a lot of typos in the slides.

автор: James LaDue

•Nov 13, 2018

Good course. A bit heavy on the theory side but if you can absorb the theory the practitioner's side is that much easier. The last topic of the course was Levy processes so the course ended just as it was getting interesting (that's not a complaint but a compliment). Would be interested in a follow-up to the course (eg. Stochastic Processes II) to expand on concepts for quantitative finance.

автор: 李美漉

•Oct 25, 2018

It is difficult to understand

автор: Ajit Coimbatore Balram

•Oct 11, 2018

I definitely think this course should be categorized as advanced instead of intermediate. The course is mathematically quite rigorous. There are a few bugs in the quizzes and the language of many of the questions can be improved. I enjoyed many of the lectures. In particular, it was nice to connect my lay man notions of some well-known stochastic processes with their precise definitions.

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