![]() Problems and Completion Checklist assigned every week, count for 48% (8% for Problems of each week) and 15% (4, 2, 2, 2, 2 & 3%, respectively) in total, respectively. To earn a certificate for the course, students must mark the score of 60% or more. Grading for the course is as below. Since the course is in self-paced, please be aware that there may be a delay in feedback to your comments from course staff. We hope this opportunity will lead to fruitful exchanges and discussion. ![]() You are invited to participate in the Discussion forum (See Forum Guidelines here) to share ideas and ask questions each peers relating to each of the course’s contents. We also hope that the participants will get some deep insights from real-world data, such as financial markets or meteorological data, using proper knowledge which will be obtained through the course. By watching the videos and answering the Problems, we hope that all participants will gain practical skills to perform numerical simulations and conduct proper data analysis needed to interpret the results. Molina (in week 6) along with a set of short Problems related to the contents of the Lecture Videos. In addition to the knowledge of introductory physics, basic knowledge of linear algebra, calculus (differential and integral), and partial differential equations must be mastered beforehand.Įach course will be provided with short Lecture Videos by the instructor, Ryoichi Yamamoto (in all weeks) and John J. Application: analysis of financial data.Simulation methods for a Brownian particle.Finally, they will analyze the simulation data according to the theories presented at the beginning of course.Īt the end of the course, we will analyze the dynamical data of more complicated systems, such as financial markets or meteorological data, using the basic theory of stochastic processes. Then, they will use these theories to develop their own python codes to perform numerical simulations of small particles diffusing in a fluid. The students will first learn the basic theories of stochastic processes. It is freely available for Windows, Mac, and Linux through the Anaconda Python Distribution. We will use the Jupyter (iPython) notebook as our programming environment. This course is an introduction to stochastic processes through numerical simulations, with a focus on the proper data analysis needed to interpret the results. This is in stark contrast to the deterministic motion of planets and stars, which can be perfectly predicted using celestial mechanics. Therefore, such motions must be modeled as stochastic processes, for which exact predictions are no longer possible. The motion of falling leaves or small particles diffusing in a fluid is highly stochastic in nature. See " Meet the Course Staff" section for more details. The original version of the course was produced and operated from March 30, 2017 to May 11, 2017. This is the second round of the course as the self-paced format. “Stochastic Processes: Data Analysis and Computer Simulation”
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