Hi all, I am using a discrete Hidden Markov Model with discrete observations in order to detect a sequence of integers. I am using the "hmm.discnp" package.
I am using the following code: signature <- c(-89, -98, -90, -84, -77, -75, -64, -60, -58, -55, -56, -57, -57, -63, -77, -81, -82, -91, -85, -89, -93) quant <- length(-110:-6) # Initialize and train the hmm with the observed sequence mentioned above. # "yval" lists the possible values for observations and K is the number of hidden states. my_hmm <- hmm(y=signature, yval=c(-110:-6), K=5) print(my_hmm) The above shows that the HMM was trained using "signature" and the values seem to be intuitive. My question is more a fundamental one in regards to understanding HMMs. I know I should use more examples of the above sequences to train the HMM in order to make it more robust. Assuming, that the HMM is trained good enough, I can use the viterbi algorithm to find the most probable sequence of hidden states. However, what I really want to find out is whether a particular observed sequence is modeled by my HMM (created above). There seems to be a viterbi() function in hmm.discnp and also mps() but both of them give them most probable hidden state sequence, whereas, I want the probability of a particular observed sequence, that is, the likelihood for an arbitrary observed sequence. This is typically solved using the solution to "Evaluation Problem" in HMMs, but I do not see a function in hmm.discnp for calculating this. Am I missing something? Thanks for the help. [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.