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0.8 0.4 PAINT 0.3 0.6 INIT CLEAN 0.2 0.4 0.4 0.1 0.45 0.2 0.2 SHOP 0.3 0.6 0.05 INITIAL PROBABILITIES TRANSITION PROBABILITIES OF HIDDEN STATES (SUNNY, RAINY) BIKE EMISSION PROBABILITIES OBSERVABLES Fig. 1 :Example of Markov Model HMM states In this case the likelihood that it is Sunny on the first day is 0.6, while the likelihood that it is Rainy is 0.4. Let's consider the activity for four days. The observation sequence is as follows: Paint, Clean, Shop and Bike. So, what is the likelihood that this observation sequence O can derive from our HMM λ, i.e P(O/A)?

0.8 0.4 PAINT 0.3 0.6 INIT CLEAN 0.2 0.4 0.4 0.1 0.45 0.2 0.2 SHOP 0.3 0.6 0.05 INITIAL PROBABILITIES TRANSITION PROBABILITIES OF HIDDEN STATES (SUNNY, RAINY) BIKE EMISSION PROBABILITIES OBSERVABLES Fig. 1 :Example of Markov Model HMM states In this case the likelihood that it is Sunny on the first day is 0.6, while the likelihood that it is Rainy is 0.4. Let's consider the activity for four days. The observation sequence is as follows: Paint, Clean, Shop and Bike. So, what is the likelihood that this observation sequence O can derive from our HMM λ, i.e P(O/A)?

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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0.8 0.4 PAINT 0.3 0.6 *************** 0.1 0.45 CLEAN INIT 0.2 0.4 0.4 0.2 0.2 SHOP 0.3 0.6 TRANSITION PROBABILITIES OF HIDDEN STATES (SUNNY, RAINY) 0.05 INITIAL PROBABILITIES BIKE EMISSION PROBABILITIES OBSERVABLES Fig. 1 Example of Markov Model HMM states In this case the likelihood that it is Sunny on the first day is 0.6, while the likelihood that it is Rainy is 0.4. Let's consider the activity for four days. The observation sequence is as follows: Paint, Clean, Shop and Bike. So, what is the likelihood that this observation sequence O can derive from our HMM A, i.e P(O/A)? ************************** ·**** ·********
Transcribed Image Text:0.8 0.4 PAINT 0.3 0.6 *************** 0.1 0.45 CLEAN INIT 0.2 0.4 0.4 0.2 0.2 SHOP 0.3 0.6 TRANSITION PROBABILITIES OF HIDDEN STATES (SUNNY, RAINY) 0.05 INITIAL PROBABILITIES BIKE EMISSION PROBABILITIES OBSERVABLES Fig. 1 Example of Markov Model HMM states In this case the likelihood that it is Sunny on the first day is 0.6, while the likelihood that it is Rainy is 0.4. Let's consider the activity for four days. The observation sequence is as follows: Paint, Clean, Shop and Bike. So, what is the likelihood that this observation sequence O can derive from our HMM A, i.e P(O/A)? ************************** ·**** ·********
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