##Introduction Hidden Markov Model (HMM) is a statistical Markov model with unobserved states. It has been widely used in different fields to solve real life problems, such as speech recognision and handwriting. This project aims to implement several basic algorithm widely used in HMM using python (numpy), including the forward-backward algorithm and Viterbi algorithm

Here is the implemention of the forward-backward algorithm and Viterbi algorithm to solve two commen Hidden Markov Model(HMM) problems with python (ipython): 1)Find the model lamda=(A,B,p) given an observation sequence O and dimensions N and M. 2)Find the hidden state sequence given an observation sequence O and model lamda=(A,B,p)

A: Transition ; B: Emission matrix; p: Initial prob. distribution;

##Reference This implementation is based on Mark Stamp's A Revealing Introduction to Hidden Markov Models (Department of Computer Science, San Jose State University)