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HMM 2

Hidden Markov Model (2. For-Backward Prob. Calculation/ Viterbi Decoding Algorithm)

Detour: Dynamic Programming - Dynamic Programming A general algorithm design technique for solving problems defined by or formulated as recurrences with overlapping sub-instances In this context, Programming == Planning - Main storyline Setting up a recurrence Relating a solution of a larger instance to solutions of some smaller instances Solve small instances once Record solutions in a table Ex..

기계학습/인공지능및기계학습개론정리 2020.11.19

Hidden Markov Model(1: Joint, Marginal Probability of HMM)

Main Questions on HMM - Given the topology of the bayseian network, HMM, or M $\pi$는 initial state, latent state를 정의할때 쓰이는 parameter a는 어느 state에서 다음 state로 transitional 할 때의 probability b는 어떤 특정 state에서 observation이 generated 되서 나올 probability X는 우리가 가지고 있는 관측값 - 1. Evaluation question - Given $\pi, a, b ,X$ - Find $P(X|M, \pi, a, b)$ - how much is X likely to be observed in the trained model? ..

기계학습/인공지능및기계학습개론정리 2020.11.16
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forward, Monte-Carlo, 빅데이터, Modeling, HMM, 역전파, 빅데이터 분석기사, MCMC, 분석기사, 몬테카를로, NLP, 자연어, Bayesian, reinforcement, pymc3, 강화학습, 밑바닥부터시작하는딥러닝3, 머신러닝, 베이지안, 앙상블,

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