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
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
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Question
Dada
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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