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Hidden Markov Model (HMM)

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Hidden Markov Model (HMM) 

VIDEO LINK: https://youtu.be/YIGCWNG8BIA
A Hidden Markov Model (HMM) is a statistical model in which the system has hidden states that cannot be directly observed, but produce observable outputs. It is based on the Markov property, meaning the next state depends only on the current state.
Video Chapters: HMM in Artificial Intelligence
00:00 Introduction
00:31 Statistical Model
00:54 HMM Examples
02:30 HMM
03:10 HMM Components
05:23 Viterbi Algorithm
06:23 HMM Applications
06:38 HMM Problems
07:28 HMM in Handwriting Recognition
11:20 Conclusion 

HMM COMPONENTS

A Hidden Markov Model (HMM) is a statistical model in which the system has hidden states that cannot be directly observed, but produce observable outputs. It is based on the Markov property, meaning the next state depends only on the current state. An HMM consists of states, observations, transition probabilities, emission probabilities, and initial probabilities. It is commonly used in applications like speech recognition, natural language processing, and bioinformatics to model sequential data.
HMM ke 3 Main Components:
Transition Probability (A)
Ek state se dusri state me jane ki probability
(Sunny → Rainy ka chance kitna?)

Emission Probability (B)
Kisi state me particular observation ka chance
(Rainy me umbrella ka chance kitna?)

Initial Probability (π)
Starting state ka probability
HMM in Artificial Intelligence || xRay Pixy ||
https://youtu.be/YIGCWNG8BIA
#optimization #algorithm #metaheuristic #robotics #deeplearning #ArtificialIntelligence #MachineLearning #computervision #research #projects #thesis #Python
#optimizationproblem #optimizationalgorithms 

 

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