The hypothesis suggested by @mbrig and @Leon that the propagation step iterates over a whole stack of cells (instead of being limited to a set of 4 direct neighbors) was correct. The following is an attempt to provide further details while answering my own questions. The problem occured at step 7, while propagating. The original … Read more
To explain by example, I’ll use an example from natural language processing. Imagine you want to know the probability of this sentence: I enjoy coffee In a Markov model, you could estimate its probability by calculating: P(WORD = I) x P(WORD = enjoy | PREVIOUS_WORD = I) x P(word = coffee| PREVIOUS_WORD = enjoy) Now, … Read more
I made a Markov chain chatbot for IRC in Python a few years back and can shed some light how I did it. The text generated does not necessarily make any sense, but it can be really fun to read. Lets break it down in steps. Assuming you have a fixed input, a text file, … Read more
Markov chains can be represented by finite state machines. The idea is that a Markov chain describes a process in which the transition to a state at time t+1 depends only on the state at time t. The main thing to keep in mind is that the transitions in a Markov chain are probabilistic rather … Read more