20100615-IBISML-makino

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HMMのベイズ的扱い方 x x x x x x x x x x x x x x x x x x x x Prior、Posterior x x x x x を x 表わすために、 x x x x HMMのパ x x x x x x x x x x x x x x x x x x ラメータ空間の上の確率分布を記述したい x x x x x x x x x Dirichlet x分布を組み x 合わせて x使う x ( 多 x 項分布のパラメータ x x 上の x 確率 x分布 ) x x x x x x x x x x x x x x x x x x x x x Transition Matrix A = x a 11 a 21 a N1 x a 1i a 2i a Ni x a 1N a 2N a NN x Emission Matrix B = x b 11 b 21 b M1 b 1i b 1N b 2i b x 2N x 32 b Mi b MN
多項分布 (multinomial distribution) x x x x x x x x x x x x x x x x x x N x 値 x 離散 x 確 x 率変 x 数 x の x確 x率分 x 布 x x x x x x x x x x p( X j | θ) x x x x x x x x x N 個のパラメータで表わされる x x x θ ( 1 ,..., N ) 確率分布としての制約を満たす x 0 1 i N i1 x x HMM の x 遷移 x行列の各列は多項分布である i j x x x x x x x x x A = x x Transition Matrix x x x x x x θ 4 θ θ x 2 x x 1 θ3 θ 5 x 1 2 3 4 5 x x x 4, 2, 3, 1, 4, 5, 2, 4, 4, 1, 5, 3, 2, x 4, 5, 5, x 4, 2, 1, 3… x a 11 a N1 x a 1i a Ni x x x x x x a 1N x a NN 33 x x x x x
Page 1 and 2: x x x x x x x x x x x x x x x x x x
Page 3 and 4: Introduction: ベイジアンとは
Page 9 and 10: Introduction: 機械学習と
Page 29 and 30: Talk Outline x x x x x x x x x x x
Page 31: HMMの行列表現 x x x x x x
Page 35 and 36: Dirichlet 分布 (2) x x x x x x x
Page 47 and 48: HMMのベイズ推定 x x x x x x
Page 49 and 50: 隠れ変数があるモデル
Page 51 and 52: 変分ベイズ法 x x x x x x x
Page 53 and 54: マルコフ連鎖モンテカ
Page 55 and 56: Talk Outline x x x x x x x x x x x
Page 57 and 58: Dirichlet Process x x x x x x x x x
Page 59 and 60: a i am he ab are and zz aaa the you
Page 61 and 62: Stick-breaking construction x x x x
Page 63 and 64: Stick-breaking construction x x x x
Page 65 and 66: Chinese Restaurant Process x x x x
Page 67 and 68: Chinese Restaurant Process x x x x
Page 69 and 70: Dirichlet Process の応用 x x x
Page 71 and 72: Dirichlet Process の応用 x x x
Page 73 and 74: そのほかの主なノンパ
Page 75 and 76: Nonparametric Bayesian HMM (1) x x
Page 77 and 78: Nonparametric Bayesian HMM (3) x x
Page 79 and 80: Infinite Hidden Markov Models x x x
Page 81 and 82: State Sequence Produced by iHMMs x
Page 83 and 84:
Hierarchical Dirichlet Process の
Page 85 and 86:
iHMM の状態の階層クラ
Page 87 and 88:
iHMM with Dirichlet Process Mixture
Page 89 and 90:
2 階層 HN-iHMM x x x x x x x x x
Page 91 and 92:
2 階層 HN-iHMMの構成 (2) x
Page 93 and 94:
2 階層以上のHN-iHMM x x x
Page 95 and 96:
実験 (1) x x x x x x x x x x x x
Page 97 and 98:
頻度結果 (2) x x x x x x x x
Page 99 and 100:
実験 1の考察 x x x x x x x
Page 101 and 102:
x x x x x x x x x x x x x x x x x x
Page 103 and 104:
x x x x x x x x x x x x x x x x x x
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20100615-IBISML-makino

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