One of the most popular ways of representing dependence in the data is through the use of Markov models, named after the Russian mathematician Andrei Andrevich Markov (1856– 1922). For models used in lossless compression, we use a specific type of Markov process called a discrete time Markov chain.
The use of the Markov model does not require the assumption of linearity. For example,
consider a binary image. The image has only two types of pixels, white pixels and black
pixels. We know that the appearance of a white pixel as the next observation depends,
to some extent, on whether the current pixel is white or black. Therefore, we can model
the pixel process as a discrete time Markov chain