I liked the way you explained Markov Chain implementation in causal DAG. Similarly, I tried to use Markov Chain model to detect anomalies in time series sequence. My findings during the testing is computation of empirical joint distribution requires enormous amount of data and is generally not feasible.The joint probability is approximated with the product of conditional probabilities based on first order Markov Chain assumption p(xi+n|xi+n-1)p(xi+n-1|xi+n-2)…p(xi+1|xi).
Please let me know if I am correct with my statement.
I liked the way you explained Markov Chain implementation in causal DAG. Similarly, I tried to use Markov Chain model to detect anomalies in time series sequence. My findings during the testing is computation of empirical joint distribution requires enormous amount of data and is generally not feasible.The joint probability is approximated with the product of conditional probabilities based on first order Markov Chain assumption p(xi+n|xi+n-1)p(xi+n-1|xi+n-2)…p(xi+1|xi).
Please let me know if I am correct with my statement.