Develop contexttree resume

bayesml/contexttree/contexttree.md

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+<!--
+Document Author
+Koshi Shimada <shimada.koshi.re@gmail.com>
+-->
+
+The stochastic data generative model is as follows:
+
+* $\mathcal{X}=\{1,2,\ldots,K\}$ : a space of a source symbol
+* $x^n = x_1 x_2 \cdots x_n \in \mathcal{X}^n~(n\in\mathbb{N})$ : an source sequence
+* $D_\mathrm{max} \in \mathbb{N}$ : the maximum depth of context tree models
+* $T$ : a context tree model, $K$-ary regular tree whose depth is smaller than or equal to $D_\mathrm{max}$, where "regular" means that all inner nodes have $K$ child nodes
+* $\mathcal{T}$ : a set of $T$
+* $s$ : a node of a context tree model
+* $\mathcal{I}(T)$ : a set of inner nodes of $T$
+* $\mathcal{L}(T)$ : a set of leaf nodes of $T$
+* $\mathcal{S}(T)$ : a set of all nodes of $T$, i.e., $\mathcal{S}(T) = \mathcal{I}(T) \cup \mathcal{L}(T)$
+* $s_T(x^{n-1}) \in \mathcal{L}(T)$ : a leaf node of $T$ corresponding to $x^{n-1} = x_1 x_2\cdots x_{n-1}$
+* $\boldsymbol{\theta}_s = (\theta_{1|s}, \theta_{2|s}, \ldots, \theta_{K|s})$ : a parameter on a leaf node, where $\theta_{k|s}$ denotes the occurrence probability of $k\in\mathcal{X}$
+
+$$
+\begin{align}
+    p(x_n | x^{n-1}, \boldsymbol{\theta}_T, T)=\theta_{x_n|s_T(x^{n-1})}.
+\end{align}
+$$
+
+The prior distribution is as follows:
+
+* $g_{0,s} \in [0,1]$ : a hyperparameter assigned to $s \in \mathcal{S}(T)$
+* $\beta_0(k|s) \in\mathbb{R}_{>0}$ : a hyperparameter of the Dirichlet distribution
+* $\boldsymbol{\beta}_0(s) = (\beta_0(1|s), \beta_0(2|s), \ldots, \beta_0(K|s)) \in\mathbb{R}^{K}_{>0}$
+* $C(\boldsymbol{\beta}_0(s)) = \frac{\Gamma\left(\sum_{k=1}^{K} \beta_0(k|s)\right)}{\prod_{k=1}^{K} \Gamma\left(\beta_0(k|s)\right)}$
+
+For $\boldsymbol{\theta}_s$ on $s\in\mathcal{L}(T)$, the Dirichlet distribution is assumed as the prior distribution as follows:
+$$
+\begin{align}
+    p(\boldsymbol{\theta}_s|T) = \mathrm{Dir}(\boldsymbol{\theta}_s|\,\boldsymbol{\beta}_0(s)) = C(\boldsymbol{\beta}_0(s)) \prod_{k=1}^{K} \theta_{k|s}^{\beta_0(k|s)-1}.
+\end{align}
+$$
+
+For $T \in \mathcal{T}$,
+$$
+\begin{align}
+    p(T)=\prod_{s \in \mathcal{I}(T)} g_{0,s} \prod_{s' \in \mathcal{L}(T)} (1-g_{0,s'}),
+\end{align}
+$$
+where $g_{0,s}=0$ if the depth of $s$ is $D_\mathrm{max}$.
+
+The posterior distribution is as follows:
+
+* $g_{n,s} \in [0,1]$ : the updated hyperparameter
+* $T_\mathrm{max}$ : a superposed context tree, $K$-ary perfect tree whose depth is $D_\mathrm{max}$
+* $s_\lambda$ : the root node
+* $\beta_n(k|s) \in\mathbb{R}_{>0}$ : a hyperparameter of the posterior Dirichlet distribution
+* $\boldsymbol{\beta}_n(s) = (\beta_n(1|s), \beta_n(2|s), \ldots, \beta_n(K|s)) \in\mathbb{R}^{K}_{>0}$
+* $I \{ \cdot \}$: the indicator function
+
+For $\boldsymbol{\theta}_s \in\mathcal{L}(T_\mathrm{max})$,
+
+$$
+\begin{align}
+    p(\boldsymbol{\theta}_s|x^n) = \mathrm{Dir}(\boldsymbol{\theta}_s|\,\boldsymbol{\beta}_n(s)) = C(\boldsymbol{\beta}_n(s)) \prod_{k=1}^{K} \theta_{k|s}^{\beta_n(k|s)-1},
+\end{align}
+$$
+
+where the updating rule of the hyperparameter is as follows:
+
+$$
+\begin{align}
+    \beta_n(k|s) = \beta_0(k|s) + \sum_{i=1}^n I \left\{ \text{$s$ is the ancestor of $s_{T_\mathrm{max}}(x^i)$} \right\}.
+\end{align}
+$$
+
+For $T \in \mathcal{T}$,
+
+$$p(T|x^{n-1})=\prod_{s \in \mathcal{I}(T)} g_{n,s} \prod_{s' \in \mathcal{L}(T)} (1-g_{n,s'}),$$
+
+where the updating rules of the hyperparameter are as follows:
+
+$$g_{n,s} =
+\begin{cases}
+    g_{0,s} & \text{if $n=0$}, \\
+    \frac{ g_{n-1,s} \tilde{q}_{s_{\mathrm{child}}} (x_n|x^{n-1}) }
+    { \tilde{q}_s(x_n|x^{n-1}) } & \text{otherwise},
+\end{cases}$$
+where $s_{\mathrm{child}}$ is the child node of $s$ on the path from $s_\lambda$ to $s_{T_\mathrm{max}}(x^n)$ and
+
+$$
+\begin{align}
+    \tilde{q}_s(x_n|x^{n-1}) =
+    \begin{cases}
+        q_s(x_n|x^{n-1}) & \text{if $s\in\mathcal{L}(T_\mathrm{max})$}, \\
+        (1-g_{n-1,s}) q_s(x_n|x^{n-1}) + g_{n-1,s} \tilde{q}_{s_{\mathrm{child}}}(x_n|x^{n-1}) & \text{otherwise}.
+    \end{cases}
+\end{align}
+$$
+
+Here,
+
+$$
+\begin{align}
+    q_s(x_n|x^{n-1}) = \frac{ \beta_{n-1}(x_n|s) }
+    {\sum_{k'=1}^{K} \beta_{n-1}(k'|s)}.
+\end{align}
+$$
+
+The predictive distribution is as follows:
+
+$$
+\begin{align}
+p(x_n|x^{n-1}) = \tilde{q}_{s_\lambda}(x_n|x^{n-1}).
+\end{align}
+$$
+
+References
+
+* T. Matsushima and S. Hirasawa, "A Class of Prior Distributions on Context Tree Models and an Efficient Algorithm of the Bayes Codes Assuming It,"
+*2007 IEEE International Symposium on Signal Processing and Information Technology*,
+2007, pp. 938-941, doi: 10.1109/ISSPIT.2007.4458049.