Introduction to Markov Processes and their applications
In probability theory and statistics, a Markov process or Markoff process, named for the Russian mathematician Andrey Markov, is a stochastic process satisfying a certain property, called the Markov property. A Markov process can be thought of as ‘memory less’: loosely speaking, a process satisfies the Markov property if one can make predictions for the future of the process based solely on its present state just as well as one could knowing the process’s full history. I.e., conditional on the present state of the system, its future and past are independent.
Markov Processes and their applications is one of the advanced topics in statistics and mainly consist of Feller, Diffusion, Affine Processes and related concepts. Our Statistics experts and Statistics online tutors being adept in these advanced concepts can cater to entire array of your needs in Markov Processes and their applications such as homework help, assignment help, dissertation help, quizzes preparation help etc. Our assignment/homework help tutors hold PhD degrees or Masters and are well versed with any referencing style, be it Harvard or APA or any other. Our experts are available 24×7 to help high school/ college/ university students with their assignments.
Following is the list of comprehensive topics in which we offer quality solutions: