Markov chains and their entropy

Abstract

Stochastic processes So far in this book we have tended to deal with one (or at most two) random variables at a time. In many concrete situations, we want to study the interaction of ‘chance’ with ‘time’, e.g. the behaviour of shares in a company on the stock market, the spread of an epidemic or the movement of a pollen grain in water (Brownian motion). To model this, we need a family of random variables (all defined on the same probability space), ( X(t) , t ≥ 0), where X(t) represents, for example, the value of the share at time t . ( X(t) , t ≥ 0) is called a (continuous time) stochastic process or random process . The word ‘stochastic’ comes from the Greek for ‘pertaining to chance’. Quite often, we will just use the word ‘process’ for short. For many studies, both theoretical and practical, we discretise time and replace the continuous interval [0, ∞) with the discrete set ℤ + = ℕ ∪ {0} or sometimes ℕ. We then have a (discrete time) stochastic process ( X n , n ∈ ℤ + ). We will focus entirely on the discrete time case in this chapter. Note . Be aware that X(t) and X t (and similarly X(n) and X n ) are both used interchangeably in the literature on this subject. There is no general theory of stochastic processes worth developing at this level. It is usual to focus on certain classes of process which have interesting properties for either theoretical development, practical application, or both of these. We will study Markov chains in this chapter.