Higher order Markov models It is customary in Markov modeling to simply expand the state space to handle higher order time relationships. Define a new
{"Image DocumentName": "Higher order Markov models", "Image ImageDescription": "It is customary in Markov modeling to simply expand the state space to handle higher order time relationships. Define a new categorical response, yit  = {yit, yi,t-1}. The order 1 transition probability, f(yit  | yi,t-1 ), is equivalent to an order 2 transition probability of the original response, f(yit | yi,t-1, yi,t-2). The conditioning events are the same, yi,t-1  = {yi,t-1, yi,t-2} yi,t-1 is completely determined by the conditioning event yi,t-1 . Expansions to higher orders can be readily accomplished in a similar fashion.", "Image XResolution": "37", "Image YResolution": "37", "Image ResolutionUnit": "Pixels/Centimeter", "Image Software": "SlidePlayer", "Image YCbCrPositioning": "Centered"}