Entropy increase and information loss in Markov models of evolution

Markov models of evolution describe changes in the probability distribution of the trait values a population might exhibit. In consequence, they also describe how entropy and conditional entropy values evolve, and how the mutual information that characterizes the relation between an earlier and a later moment in a lineage's history depends on how much time separates them. These models therefore provide an interesting perspective on questions that usually are considered in the foundations of physics-when and why does entropy increase and at what rates do changes in entropy take place? They also throw light on an important epistemological question: are there limits on what your observations of the present can tell you about the evolutionary past?.