Hidden Markov Model - 2 States

hmm.rb

# The model here is a two state HMM (States are A and B).
# This is a simple network having 4 possible actions,
# two for each state (going to the other or staying at the same)

# pa and pb are probabilities of having next state as A
# from state A and B respectively
class Hmm
  def initialize(pa,pb)
    @pa = pa; @pb = pb
  end
  def prob(n)
    if n == 0
      1
    else
      prev = prob(n-1) # Memoization can be used for optimization
      prev * @pa + (1-prev) * @pb
    end
  end
end

hmm = Hmm.new(0.6,0.2)
puts hmm.prob(3)
#(1..20).each{|i| puts hmm.prob(i)}