20100615-IBISML-makino

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iHMM の学習 x x x x x x x x x x x x x x x x 変分ベイズ法 (Beal, 2003) x x x x x x x x x x x x x状態数を x 有限 x 個 ( 多めに) x x置いて近 x 似する x Blocked x Gibbs x Sampler x x (Teh+, x 2006) x x x x x x x Stick-Breaking x process x を使い、 x遷移行 x列を sampling x 状態が無限個あるため、Forward-backward が利用不 x能 x Beam Sampler x (Van x Gael+, 2008) x x x Collapsed x Gibbs Sampler x Chinese Restaurant Process を使い、遷移行列の期待分布を計算 Conditional Simultaneous Draws Sampler (Makino+, x 2009) x x x x x x x x x x x x x x x x x x x x x x x x x x x 82 x x x x x
Hierarchical Dirichlet Process の応用 (1) x x x x x x x x x x x x x x x x x x x x 0 x x x 文 x 書 x のトピックモデル x x x x x (Teh, x Jordan, x x Beal&Blei x 2004) G x x x x x x x x x 0 x x x x x x x x x Latent Dirichlet Allocation (Blei, Ng&Jordan 2003) x x x x x x x x のノンパラメトリック x 版 x x x トピック数 xK を明 x示的に与えず、 x 各 x文書のトピック分布を生成する x x x x データ ( 文書集 x 合 ) 観測後の事後 x x分布を計算することで、 x 最適 x なトピック数の分布とともに、各トピックの単語分布を推定できる x x x x x β x x x x x x x G j z ji w ji x N 83 x xM x x
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Introduction: ベイジアンとは
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Introduction: 機械学習と
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Talk Outline x x x x x x x x x x x
Page 31 and 32: HMMの行列表現 x x x x x x
Page 33 and 34: 多項分布 (multinomial distri
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: State Sequence Produced by iHMMs x
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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