Short observation sequence: ['no', 'no', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'yes'] Filtering - distribution over most recent state: sunny 0.142 rainy 0.600 foggy 0.258 Prediction - distribution over next state: sunny 0.285 rainy 0.445 foggy 0.270 Smoothing - distribution over state at each point in time: time 0 sunny 0.747 rainy 0.042 foggy 0.210 time 1 sunny 0.718 rainy 0.033 foggy 0.249 time 2 sunny 0.589 rainy 0.067 foggy 0.344 time 3 sunny 0.356 rainy 0.318 foggy 0.326 time 4 sunny 0.470 rainy 0.115 foggy 0.415 time 5 sunny 0.352 rainy 0.151 foggy 0.496 time 6 sunny 0.100 rainy 0.601 foggy 0.300 time 7 sunny 0.083 rainy 0.672 foggy 0.245 time 8 sunny 0.223 rainy 0.335 foggy 0.441 time 9 sunny 0.142 rainy 0.600 foggy 0.258 Viterbi - predicted state sequence: ['sunny', 'sunny', 'sunny', 'sunny', 'sunny', 'sunny', 'rainy', 'rainy', 'rainy', 'rainy'] Viterbi - actual state sequence: ['foggy', 'foggy', 'foggy', 'rainy', 'sunny', 'foggy', 'rainy', 'rainy', 'foggy', 'rainy'] The accuracy of your viterbi classifier on the short data set is 0.4 The accuracy of your viterbi classifier on the entire data set is 0.636