Mean Field Approximation

基于概率图模型的近似推断方法大概分为三种:(1) Mean field approximation; (2) Belief propagation; (3) Monte Carlo Sampling. —-林达华


In mean field theory, a complex probabilistic model is approximated by a set of individual models defined on each vertex. It overlooks the interaction within cliques and averages the interactive effect among the vertices. Mathematically, it approximates the joint distribution \(p^*(\mathbf{z})\) by the product of factorized distributions: \(q(\mathbf{z}) \approx \prod_{i=1}^d q_i(z_i)\) by minimizing the KL divergence. Throughout the writing, we are going to elaborate mean field approximation based on Ising model.


The objective is mean field inference is to minimize the KL divergence:

\[\min_{q_1, ..., q_n} KL(q \| p^*) = KL(\prod_{i=1}^d q_i(z_i) \| p^*).\]

Steps are taken iteratively by optimizing \(q_k\) while fixing other factorized distributions until convergence, i.e.,

\[\min_{q_k} KL(q \| p^*) = KL(\prod_{i=1}^d q_i(z_i) \| p^*).\] \[KL(\prod_{i=1}^d q_i(z_i) \| p^*) = \int \prod_{i=1}^d q_i \log \frac{\prod_{i=1}^d q_i}{p^*} d \mathbf{z} \\ = \sum_{i=1}^d \int \log q_i \prod_{j=1}^d q_j d \mathbf{z} - \int \prod_{j=1}^d q_j \log p^* d \mathbf{z} \\ = \int \log q_k \prod_{j=1}^d q_j d \mathbf{z} + \sum_{i \neq k} \int \log q_i \prod_{j=1}^d q_j d \mathbf{z} - \int \prod_{j=1}^d q_j \log p^* d \mathbf{z} \\\] \[\left( \int \log q_k \prod_{j=1}^d q_j d \mathbf{z} = \int q_k \log q_k \int \prod_{j \neq k}^d q_j d z_{\neq k} d z_k \\ = \int q_k \log q_k d z_k \right)\] \[\left( \sum_{i \neq k} \int \log q_i \prod_{j=1}^d q_j d \mathbf{z} = const\right)\] \[= \int q_k \log q_k d z_k - \int q_k \int \prod_{j\neq k} q_j \log p^* d z_{\neq k} d z_k + const \\ = \int q_k (\log q_k - \int \prod_{j\neq k} q_j \log p^* d z_{\neq k}) d z_k + const\] \[\left( h(z_k) = \int \prod_{j\neq k} q_j \log p^* d z_{\neq k} = E_{q_{-k}}(\log p^*) \right)\] \[\left(t(z_k) = e^{h(z_k)} = E_{q_{-k}}(p^*) \right)\] \[= \int q_k \log \frac{q_k}{t} d z_k + const\]

So in each optimization step, we minimize \(KL(q_k \| t)\).

\[\log q_k = \log t(z_k) = h(z_k) = E_{q_{-k}}(\log p^*) + const,\]

where the constant is added to ensure a legal distribution.

Mean Field Inference on Ising Model

Ising model is defined by

\[p(y) \propto exp(\frac{1}{2} J \sum_i \sum_{j \in Nbr(i)} y_i y_j + \sum_i b_i y_i ),\]

where \(y_i \in \{1, -1 \}\).

Then we approximate the distribution by mean field \(p(y) \approx q(y) = \prod_i q_i(y_i)\).

\[log(q_k(y_k)) = E_{q_{-k}} \log p(y) + const \\ = E_{q_{-k}} (J \sum_{j \in Nbr(k)} y_k y_j + b_k y_k) + const \\ = J \sum_{j \in Nbr(k)} y_k E_{q_{-k}}(y_j) + b_k y_k + const \\ = J y_k \sum_{j \in Nbr(k)} E(y_j) + b_k y_k + const \\ = y_k (J \sum_{j \in Nbr(k)} E(y_j) + b_k) + const \\ = y_k M + const\] \[\left( q_k(y_k=1) + q_k(y_k=-1) = C exp(M) + C exp(-M) = 1 \right)\] \[\left( C = \frac{1}{exp(M) + exp(-M)} = \right)\] \[q_k(y_k=1) = \frac{exp(M)}{exp(M) + exp(-M)} = \frac{1}{1+exp(-2M)} = sigmoid(2M)\] \[q_k(y_k=-1) = \frac{exp(-M)}{exp(M) + exp(-M)} = \frac{1}{1+exp(2M)} = sigmoid(-2M)\] \[E(y_k) = q_k(y_k=1) - q_k(y_k=-1) =\frac{exp(M) - exp(-M)}{exp(M) + exp(-M)} = tanh(M)\]