Final answer:
The 'most likely explanation' in Markov chains is the sequence of states with the highest probability of producing observed data, determined through methods like maximum likelihood estimation or other statistical approaches such as Bayesian inference.
Step-by-step explanation:
In the context of Markov chains, the term "most likely explanation" often refers to the sequence of states that has the highest probability of producing the observed data, given a particular model. This approach is akin to the maximum likelihood estimation method used in statistics, which seeks to find the model parameters that make the observed data most probable. The explanation, or inference, is constructed based on the maximum likelihood or, in other interpretations, through perspectives such as Bayesian inference, abduction, or maximum parsimony.
It's important to note that determining the most likely explanation in Markov chains and related models takes into account the probability of a particular sequence of events or states, rather than just a single event. Various techniques, such as the Viterbi algorithm, can be employed to find the most likely sequence of states that a Markov chain would traverse, given a set of observations.