Chapter 4 Inference for Lattice Models

4.1 Parameter Estimation for Lattice Models

4.1.1 Pseudolikelihood

Pseudolikelihood
Pseudolikelihood is to be writen as $$$p(\boldsymbol{\eta})=\prod_{i=1}^{n} \mathbf{P}\left(z\left(s_{i}\right) \mid\left\{z\left(s_{j}\right): j \neq i\right\} ; \boldsymbol{\eta}\right) \tag{4.1}$$$ Also denote the negative log-pseudolikelihood function as $$$P(\boldsymbol{\eta})=-\log (p(\boldsymbol{\eta}))=-\sum_{i=1}^{n} \log \mathbf{P}\left(z\left(s_{i}\right) \mid\left\{z\left(s_{j}\right): j \neq i\right\} ; \boldsymbol{\eta}\right) \tag{4.2}$$$

The goal is to maximizing p seudolikelihood (??). Equally, we aims to find $$\hat \eta_p$$ which minimize formula (4.2).

4.1.2 Gaussian Maximum Likelihood Estimation

Under Gaussian assumptions, The SG and CG model can be estimated by maximum likelihood method.

• If $$\mathbf{Z} \sim \mathrm{N}(\boldsymbol{\mu}, \sigma)$$, the negative log-likelihood is $$$L(\boldsymbol{\eta})=\frac{n}{2} \log (2 \pi)+\frac{1}{2} \log (|\Sigma|)+\frac{1}{2}(\mathbf{z}-\boldsymbol{\mu})^{\top} \Sigma^{-1}(\mathbf{z}-\boldsymbol{\mu}) \tag{4.3}$$$
• For the covariance matrix, $$$\begin{array}{ll} \text { SG : } & \Sigma=(I-B)^{-1} \Lambda\left(I-B^{\prime}\right)^{-1} \\ \text { CG : } & \Sigma=(I-C)^{-1} M \end{array} \tag{4.4}$$$

4.2 Conditional Gaussian Model

Recall Conditional Gaussian Model introduced in section 3.2.2.2. The negative log-pseudolikelihood function here is $$$P(\boldsymbol{\eta})=\frac{1}{2} \sum_{i=1}^{n} \frac{\left(z\left(s_{i}\right)-\theta\left(\left\{z\left(s_{j}\right): j \neq i\right\}\right)\right)^{2}}{\tau_{i}^{2}}+\log \left(2 \pi \tau_{i}^{2}\right) \tag{4.5}$$$ where $$\theta\left(\left\{z\left(s_{j}\right): j \neq i\right\}\right)=\mu_{i}+\sum_{j=1}^{n} c_{i j}\left(z\left(s_{j}\right)-\mu_{j}\right) .$$

It can be seen from forumula (4.5) that

• The maximum pseudolikelihood estimator is a weighted least squares estimator under heterogeneous variances $$\tau_i$$
• If $$\tau_{i}^{2} \equiv \sigma^{2}$$ for all $$i=1, \cdots, n$$, then maximum pseudolikelihood estimator is the ordinary least squares estimator.

Now that we have the goal function, MLE illustrated in section 4.1.2 can be applied for parameter estimation

4.3 Auto-logistic Model

The conditional distribution is $$$\operatorname{logit}\left(Z\left(s_{i}\right)=1 \mid\left\{z\left(s_{j}\right): j \neq i\right\}\right)=\alpha_{i}+\sum_{j=1}^{n} \theta_{i j} z\left(s_{j}\right) . \tag{4.6}$$$

Consider again the isotropic Ising model $$\gamma_{1}=\gamma_{2}=\gamma$$, the model is simplified to $$$\operatorname{logit}\left(Z\left(s_{i}\right)=1 \mid\left\{z\left(s_{j}\right): j \neq i\right\}\right)=\alpha+\gamma n_{i} . \tag{4.7}$$$

Estimation: parameters can be estimated by a logistic regression with pseudo-data $$\left\{\left(z_{i}, n_{i}\right), i=1, \cdots, n\right\}$$.

4.4 Auto-Poisson Model

The negative log-likelihood

\begin{aligned} \mathbf{P}\left(\left\{\alpha_{i}\right\},\left\{\theta_{i j}\right\}\right) &=\sum_{i=1}^{n} \lambda_{i}\left(\left\{z\left(s_{j}\right): j \neq i\right\}\right) \\ &-z\left(s_{i}\right) \log \left(\lambda_{i}\left(\left\{z\left(s_{j}\right): j \neq i\right\}\right)\right)+\log \left(z\left(s_{i}\right) !\right), \end{aligned} \tag{4.8} which is a Poisson regression model.