Stochastic Processes Courses
Learn about the stochastic processes and more on edX today!
What are Stochastic Processes?
A stochastic process is a section of probability theory dealing with random variables. The stochastic process involves random variables changing over time. Stochastic processes are a standard tool for mathematicians, physicists, and others in the field. Common usages include option pricing theory to modeling the growth of bacterial colonies. It's a useful continuous-time process where time t defines a collection of variables and corresponds to those variables over each time point. Two of the most famous applications of stochastic models include the Poisson process, used to study the number of phone calls during a period of time, and the Wiener process or Brownian Motion Process, used to analyze price changes. Applications also include use cases in biology, chemistry, economics, research fields, and a number of others.
Learn Stochastic Processes
Stochastic Processes help make sense of randomness. Probability distribution helps us make logical conclusions about things that seem random. Theories of stochastic processes help us build in standard deviations and understand both discrete-time and continuous-time processes as well as stationarity.
Stochastic Processes Courses and Certifications
edX offers courses in partnership with leaders in the mathematics and statistics fields. Kyoto University offers an introductory course in stochastic processes. It includes the definition of a stochastic process and introduces you to the fundamentals of discrete-time processes and continuous-time processes, the principles of Poisson processes, Gaussian processes, and others. EPFL offers more practical applications of Stochastic processes with their course Neuronal Dynamics. You'll learn how random processes, differential equations, and separation of time scales can be used to understand the dynamics of neurons. Harvard University also has a practical approach to statistical models using stochastic processes, among other theories. The collection of random variables in probability space occupies a large part of statistical models, so you'll be able to approach complex problems with ease.
Stochastic Processes and Other Theories
The theory of stochastic processes opens up a variety of applications where probability and random numbers can help manage large problems. Whether it's the Poisson distribution or other conditional probabilities, your knowledge and expertise can open up research and applications in a wide variety of areas. The characteristic functions of random processes have applications in fields as different as biology and economics. Put that expertise to use building solutions for your research field and help us understand these state spaces and finite-dimensional distributions. It's an exciting discipline that can help us untangle complex, seemingly random processes with ease.