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# An Introduction to Hidden Markov Model

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• 一開始的 A, B 機率是自行指定的，一般來說就是平均分配
然後利用 Baum-Welch 的方式不斷的更新 A, B 矩陣的值

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• Hi , 謝謝分享這份投影片, 非常淺顯易懂
但還是有個問題, 一開始矩陣 A , B 的機率要如何決定呢?
我查了一些資料但是都沒有提到一開始 hidden state 間轉移的機率
以及 各種 possible output 在各個 state 中的 distribution 要如何決定?
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• 1. An Introduction to HMM Browny 2010.07.21
• 2. MM vs. HMM States States Observations
• 3. Markov Model • Given 3 weather states: – {S1, S2, S3} = {rain, cloudy, sunny} Rain Cloudy Sunny Rain 0.4 0.3 0.3 Cloudy 0.2 0.6 0.2 Sunny 0.1 0.1 0.8 • What is the probabilities for next 7 days will be {sun, sun, rain, rain, sun, cloud, sun} ?
• 4. Hidden Markov Model • The states – We don’t understand, Hidden! – But it can be indirectly observed • Example – 北極or赤道(model), Hot/Cold(state), 1/2/3 ice cream(observation)
• 5. Hidden Markov Model • The observation is a probability function of state which is not observable directly Hidden States
• 6. HMM Elements • N, the number of states in the model • M, the number of distinct observation symbols • A, the state transition probability distribution • B, the observation symbol probability distribution in states • π, the initial state distribution λ: model
• 7. Example P(…|C) P(…|H) P(…|Start) P(1|…) 0.7 0.1 P(2|…) 0.2 B: 0.2 Observation P(3|…) 0.1 0.7 P(C|…) 0.8 0.1 0.5 A: π: P(H|…) 0.1 0.8 0.5 Transition initial P(STOP|…) 0.1 0.1 0
• 8. 3 Problems
• 9. 3 Problems 1. 觀察到的現象最符合哪一個模型 P(觀察到的現象|模型) 2. 怎樣的狀態序列最符合觀察到的現 象和已知的模型 P(狀態序列|觀察到的現象, 模型) 3. 怎樣的模型最有可能產生觀察到的 現象 what 模型 maximize P(觀察到的現象| 模型)
• 10. Solution 1 • 已知模型，一觀察序列之產生機率 P(O|λ) R1 R1 R1 S1 S1 S1 R2 R2 R2 S2 R1 S2 R1 S2 R1 R2 R2 R2 S3 R1 S3 R1 S3 R1 R2 R2 R2 1 2 3 t 觀察到 R1 R1 R2 的機率為多少？
• 11. Solution 1 • 考慮一特定的狀態序列 Q = q1, q2 … qT • 產生出一特定觀察序列之機率為 P(O|Q, λ) = P(O1|q1, λ) * P(O2|q2, λ) * … * P(Ot|qt, λ) = bq1(O1) * bq2(O2) * … * bqT(OT)
• 12. Solution 1 • 此一特定序列發生之機率為 P(Q|λ) = πq1 * aq1q2 * aq2q3 * … * aq(T-1)qT • 已知模型，一觀察序列之產生機率 P(O|λ) P(O|λ) = ,q∑ q P(O|Q, λ) * P(Q| λ) q ,..., 1 2 T = q ,q∑ q πq1 * bq1(O1) * aq1q2 bq2(O2)* … * aq(T-1)qT * bqT(OT) ,..., 1 2 T
• 13. Solution 1 狀態的數量) • Complexity (N: 狀態的數量 – 2T*NT ≈ (2T-1)NT muls + NT-1 adds (NT:狀態轉換 組合數) – For N=5 states, T=100 observations, there are order 2*100*5100 ≈ 1072 computations!! • Forward Algorithm – Forward variable αt(i) (給定時間 t 時狀態為 Si 的 條件下，向前 向前局部觀察序列為O1, O2, O3…, Ot 的 向前 機率) at (i ) = P(O1 , O2 ,..., Ot , qt = Si | λ )
• 14. Solution 1 R1 R1 R1 S1 S1 S1 R2 R2 R2 When O1 = R1 S2 R1 S2 R1 S2 R1 R2 R2 R2 S3 R1 S3 R1 S3 R1 R2 R2 R2 1 2 3 t α1 ( i ) = π i bi ( O1 ) 1 ≤ i ≤ N α 2 (1) = α1 (1) a11 + α1 ( 2 ) a21 + α1 ( 3) a31  b1 ( O2 )   α1 (1) = π 1b1 (O1 ) α1 (2) = π 2b2 (O1 ) α 2 ( 2 ) = α1 (1) a12 + α1 ( 2 ) a22 + α1 ( 3) a32  b2 ( O2 )   α1 (3) = π 3b3 (O1 )
• 15. Forward Algorithm • Initialization: α1 (i ) = π i bi (O1 ) 1 ≤ i ≤ N • Induction: N  1 ≤ t ≤ T −1 αt +1 ( j ) = ∑αt ( i ) aij  bj ( Ot +1 ) 1 ≤ j ≤ N  i=1  • Termination: N P(O | λ ) = ∑ αT (i ) i =1
• 16. Backward Algorithm • Forward Algorithm at (i ) = P(O1 , O2 ,..., Ot , qt = Si | λ ) • Backward Algorithm – 給定時間 t 時狀態為 Si 的條件下，向後 向後局 向後 部觀察序列為 Ot+1, Ot+2, …, OT的機率 βt (i ) = P(Ot +1 , Ot + 2 ,..., OT , qt = Si | λ )
• 17. Backward Algorithm • Initialization βT (i ) = 1 1 ≤ i ≤ N • Induction N t = T −1, T − 2, ...,1 βt (i ) = ∑ aij b j (Ot +1 ) β t +1 ( j ) j =1 1≤ i ≤ N
• 18. Backward Algorithm R1 R1 R1 S1 S1 S1 R2 R2 R2 When OT = R1 S2 R1 S2 R1 S2 R1 R2 R2 R2 S3 R1 S3 R1 S3 R1 R2 R2 R2 1 2 3 t N β T −1 (1) = ∑ a1 j b j ( OT ) β T ( j ) j =1 = a11b1 ( OT ) + a12 b2 ( OT ) + a13b3 ( OT )
• 19. Solution 2 • 怎樣的狀態序列最能解釋觀察到的現 象和已知的模型 P(狀態序列|觀察到的現象, 模型) • 無精確解，有很多種方式解此問題， 對狀態序列的不同限制有不同的解法 對狀態序列的不同限制
• 20. Solution 2 • 例: Choose the state qt which are individually most likely – γt(i) : the probability of being in state Si at time t, given the observation sequence O, and the model λ P (O | qt = Si , λ ) α t ( i ) βt ( i ) α t ( i ) βt ( i ) γ t (i ) = = = N P (O λ ) P (O λ ) ∑ α t ( i ) βt ( i ) i =1 qt = argmax γ t ( i )  1 ≤ t ≤ T   1≤i ≤ N
• 21. Viterbi algorithm • The most widely used criteria is to find the “single best state sequence” maxmize P ( Q | O, λ ) ≈ maxmize P ( Q, O | λ ) • A formal technique exists, based on dynamic programming methods, and is called the Viterbi algorithm
• 22. Viterbi algorithm • To find the single best state sequence, Q = {q1, q2, …, qT}, for the given observation sequence O = {O1, O2, …, OT} • δt(i): the best score (highest prob.) along a single path, at time t, which accounts for the first t observations and end in state Si δ t ( i ) = max P  q1 q2 ... qt = Si , O1 O2 ... Ot λ    1 q , q ,..., q 2 t −1
• 23. Viterbi algorithm • Initialization - δ1(i) – When t = 1 the most probable path to a state does not sensibly exist – However we use the probability of being in that state given t = 1 and the observable state O1 δ1 ( i ) = π i bi ( O1 ) 1 ≤ i ≤ N ψ (i ) = 0
• 24. Viterbi algorithm • Calculate δt(i) when t > 1 – δt(i) : The most probable path to the state X at time t – This path to X will have to pass through one of the states A, B or C at time (t-1) Most probable path to A: δ t −1 ( A) a AX bX ( Ot )
• 25. Viterbi algorithm • Recursion δ t ( j ) = max δ t −1 ( i ) aij  b j ( Ot )   2≤t ≤T 1≤ i ≤ N ψ t ( j ) = argmax δ t −1 ( i ) aij  1≤ j ≤ N  1≤ i ≤ N  • Termination P* = max δ T ( i )    1≤i ≤ N q = argmax δ T ( i )  * T   1≤i ≤ N
• 26. Viterbi algorithm • Path (state sequence) backtracking qt* = ψ t +1 (qt*+1 ) t = T − 1, T − 2, ..., 1 qT −1 = ψ T (qT ) = argmax δ T −1 ( i ) aiq*  * * 1≤i ≤ N  T  ... ... * * q1 = ψ 2 (q2 )
• 27. Solution 3 • 怎樣的模型 λ = (A, B, π) 最有可能產生 觀察到的現象 what 模型 maximize P(觀察到的現象| 模型) • There is no known analytic solution. We can choose λ = (A, B, π) such that P(O| λ) is locally maximized using an iterative procedure
• 28. Baum-Welch Method • Define ξt(i, j) = P(qt=Si , qt+1=Sj|O, λ) – The probability of being in state Si at time t, and state Sj at time t+1 α t ( i ) aij b j ( Ot +1 ) βt +1 ( j ) ξt ( i, j ) = P (O λ ) α t ( i ) aij b j ( Ot +1 ) βt +1 ( j ) = N N ∑∑ α ( i ) a b ( O ) β ( j ) i =1 j =1 t ij j t +1 t +1
• 29. Baum-Welch Method • γt(i) : the probability of being in state Si at time t, given the observation sequence O, and the model λ α t ( i ) βt ( i ) α ( i ) βt ( i ) γ t (i ) = = N t P (O λ ) ∑ α t ( i ) βt ( i ) • Relate γt(i) to ξt(i, j) i =1 α t ( i ) aij b j ( Ot +1 ) βt +1 ( j ) N ξt ( i, j ) = γ t ( i ) = ∑ ξt ( i, j ) P (O λ ) j =1 α t ( i ) aij b j ( Ot +1 ) βt +1 ( j ) = N N ∑∑ α ( i ) a b ( O ) β ( j ) i =1 j =1 t ij j t +1 t +1
• 30. Baum-Welch Method • The expected number of times that state Si is visited T −1 ∑ γ ( i ) = Expected number of transitions from Si t =1 t • Similarly, the expected number of transitions from state Si to state Sj T −1 ∑ ξ ( i, j ) = Expected number of transitions from S to S t =1 t i j
• 31. Baum-Welch Method • Re-estimation formulas for π, A and B π i = γ1(i) T −1 ∑ξ (i, j) t =1 t expected number of transitions from state Si to S j aij = T −1 = expected number of transitions from state Si ∑γt (i) t =1 T ∑t =1 γ t ( j) s.t. Ot =vk expected number of times in state j and observing symbol vk b j (k) = T = expected number of times in state j ∑γ ( j) t =1 t
• 32. Baum-Welch Method • P(O|λ) > P(O|λ) • Iteratively use λ in place of λ and repeat the re-estimation, we then can improve P(O| λ) until some limiting point is reached