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The INLA method is used to compute fast approximative posterior distribution for Bayesian generalised additive models. The hierarchical structure of such a model with latent Gaussian components \(\boldsymbol{u}\), covariance parameters \(\boldsymbol{\theta}\), and measured response variables \(\boldsymbol{y}\), can be written as \[ \begin{aligned} \boldsymbol{\theta} &\sim p(\boldsymbol{\theta}) \\ \boldsymbol{u}|\boldsymbol{\theta} &\sim \mathcal{N}\!\left(\boldsymbol{\mu}_u, \boldsymbol{Q}(\boldsymbol{\theta})^{-1}\right) \\ \boldsymbol{\eta}(\boldsymbol{u}) &= \boldsymbol{A}\boldsymbol{u} \\ \boldsymbol{y}|\boldsymbol{u},\boldsymbol{\theta} & \sim p(\boldsymbol{y}|\boldsymbol{\eta}(\boldsymbol{u}),\boldsymbol{\theta}) \end{aligned} \] where typically each linear predictor element, \(\eta_i(\boldsymbol{u})\), is linked to a location parameter of the distribution for observation \(y_i\), for each \(i\), via a (non-linear) link function \(g^{-1}(\cdot)\). In the R-INLA implementation, the observations are assumed to be conditionally independent, given \(\boldsymbol{\eta}\) and \(\boldsymbol{\theta}\).
The premise for the inlabru method for non-linear predictors is to build on the existing implementation, and only add a linearisation step. The properties of the resulting approximation will depend on the nature of the non-linearity.
Let \(\widetilde{\boldsymbol{\eta}}(\boldsymbol{u})\) be a non-linear predictor, i.e. a deterministic function of \(\boldsymbol{u}\), \[ \widetilde{\boldsymbol{\eta}} (\boldsymbol{u}) = \textsf{fcn} (\boldsymbol{u}), \] and let \(\overline{\boldsymbol{\eta}}(\boldsymbol{u})\) be the 1st order Taylor approximation at \(\boldsymbol{u}_0\), \[ \overline{\boldsymbol{\eta}}(\boldsymbol{u}) = \widetilde{\boldsymbol{\eta}}(\boldsymbol{u}_0) + \boldsymbol{B}(\boldsymbol{u} - \boldsymbol{u}_0) = \left[\widetilde{\boldsymbol{\eta}}(\boldsymbol{u}_0) - \boldsymbol{B}\boldsymbol{u}_0\right] + \boldsymbol{B}\boldsymbol{u} , \] where \(\boldsymbol{B}\) is the derivative matrix for the non-linear predictor, evaluated at \(\boldsymbol{u}_0\). Hence, we define \[ \begin{aligned} \boldsymbol{y} | \boldsymbol{u}, {\boldsymbol{\theta}} &\overset{d}{=} \boldsymbol{y} | \widetilde{\boldsymbol{\eta}}(\boldsymbol{u}), {\boldsymbol{\theta}} \\ &\sim p (\boldsymbol{y} | g^{-1}[\widetilde{\boldsymbol{\eta}}(\boldsymbol{u})], {\boldsymbol{\theta}})\\ \end{aligned} \] The non-linear observation model \(p(\boldsymbol{y}|g^{-1}[\widetilde{\boldsymbol{\eta}}(\boldsymbol{u})],\boldsymbol{\theta})\) is approximated by replacing the non-linear predictor with its linearisation, so that the linearised model is defined by
\[ \overline{p}(\boldsymbol{y}|\boldsymbol{u},\boldsymbol{\theta}) = p(\boldsymbol{y}|\overline{\boldsymbol{\eta}}(\boldsymbol{u}),\boldsymbol{\theta}) = p(\boldsymbol{y}|g^{-1}[\overline{\boldsymbol{\eta}}(\boldsymbol{u})],\boldsymbol{\theta}) \approx p(\boldsymbol{y}|g^{-1}[\widetilde{\boldsymbol{\eta}}(\boldsymbol{u})],\boldsymbol{\theta}) = p(\boldsymbol{y}|\widetilde{\boldsymbol{\eta}}(\boldsymbol{u}),\boldsymbol{\theta}) = \widetilde{p}(\boldsymbol{y}|\boldsymbol{u},\boldsymbol{\theta}) \] The non-linear model posterior is factorised as \[ \widetilde{p}(\boldsymbol{\theta},\boldsymbol{u}|\boldsymbol{y}) = \widetilde{p}(\boldsymbol{\theta}|\boldsymbol{y})\widetilde{p}(\boldsymbol{u}|\boldsymbol{y},\boldsymbol{\theta}), \] and the linear model approximation is factorised as \[ \overline{p}(\boldsymbol{\theta},\boldsymbol{u}|\boldsymbol{y}) = \overline{p}(\boldsymbol{\theta}|\boldsymbol{y})\overline{p}(\boldsymbol{u}|\boldsymbol{y},\boldsymbol{\theta}). \]
The remaining step of the approximation is how to choose the linearisation point \(\boldsymbol{u}_*\). For a given linearisation point \(\boldsymbol{v}\), INLA will compute the posterior mode for \(\boldsymbol{\theta}\), \[ \widehat{\boldsymbol{\theta}}_{\boldsymbol{v}} = \mathop{\mathrm{arg\,max}}_{\boldsymbol{\theta}} \overline{p}_\boldsymbol{v} ( {\boldsymbol{\theta}} | \boldsymbol{y} ), \] and the joint conditional posterior mode for \(\boldsymbol{u}\), \[ \widehat{\boldsymbol{u}}_{\boldsymbol{v}} = \mathop{\mathrm{arg\,max}}_{\boldsymbol{u}} \overline{p}_\boldsymbol{v} ( \boldsymbol{u} | \boldsymbol{y}, \widehat{\boldsymbol{\theta}}_{\boldsymbol{v}} ) . \]
Define the Bayesian estimation functional1 \[ f(\overline{p}_{\boldsymbol{v}}) = (\widehat{\boldsymbol{\theta}}_{\boldsymbol{v}},\widehat{\boldsymbol{u}}_{\boldsymbol{v}}) \] and let \(f(p)=(\widehat{\boldsymbol{\theta}},\widehat{\boldsymbol{u}})\) denote the corresponding posterior modes for the true posterior distribution, \[ \begin{aligned} \widehat{{\boldsymbol{\theta}}} &= \mathop{\mathrm{arg\,max}}_{\boldsymbol{\theta}} p ( {\boldsymbol{\theta}} | \boldsymbol{y} ), \\ \widehat{\boldsymbol{u}} &= \mathop{\mathrm{arg\,max}}_{\boldsymbol{u}} p (\boldsymbol{u} | \boldsymbol{y}, \widehat{{\boldsymbol{\theta}}}). \end{aligned} \]
The fixed point \((\boldsymbol{\theta}_*,\boldsymbol{u}_*)=f(\overline{p}_{\boldsymbol{u}_*})\) should ideally be close to \((\widehat{\boldsymbol{\theta}},\widehat{\boldsymbol{u}})\), i.e. close to the true marginal/conditional posterior mode. We can achieve this for the conditional latent mode, so that \(\boldsymbol{u}_*=\mathop{\mathrm{arg\,max}}_{\boldsymbol{u}} p (\boldsymbol{u} | \boldsymbol{y}, \widehat{\boldsymbol{\theta}}_{\boldsymbol{u}_*})\).
We therefore seek the latent vector \(\boldsymbol{u}_*\) that generates the fixed point of the functional, so that \((\boldsymbol{\theta}_*,\boldsymbol{u}_*)=f(\overline{p}_{\boldsymbol{u}_*})\).
One key to the fixed point iteration is that the observation model is linked to \(\boldsymbol{u}\) only through the non-linear predictor \(\widetilde{\boldsymbol{\eta}}(\boldsymbol{u})\), since this leads to a simplified line search method below.
A potential improvement of step 4 might be to also take into account the prior distribution for \(\boldsymbol{u}\) as a minimisation penalty, to avoid moving further than would be indicated by a full likelihood optimisation.
In step 4, we would ideally want \(\alpha\) to be
\[
\mathop{\mathrm{arg\,max}}_{\alpha} \left[\ln
p(\boldsymbol{u}|\boldsymbol{y},\boldsymbol{\theta}_1)\right]_{\boldsymbol{u}=\boldsymbol{v}_\alpha}.
\] However, since this requires access to the internal likelihood
and prior density evaluation code, we instead use a simpler alternative.
We consider norms of the form \(\|\widetilde{\eta}(\boldsymbol{v}_\alpha)-\overline{\eta}(\boldsymbol{u}_1)\|\)
that only depend on the nonlinear and linearised predictor expressions,
and other known quantities, given \(\boldsymbol{u}_0\), such as the current
INLA estimate of the component wise predictor variances.
Let \(\sigma_i^2 =
\mathrm{Var}_{\boldsymbol{u}\sim
\overline{p}(\boldsymbol{u}|\boldsymbol{y},\boldsymbol{\theta}_1)}(\overline{\boldsymbol{\eta}}_i(\boldsymbol{u}))\)
be the current estimate of the posterior variance for each predictor
element \(i\). We then define an inner
product on the space of predictor vectors as \[
\langle \boldsymbol{a},\boldsymbol{b} \rangle_V
=
\sum_i \frac{a_i b_i}{\sigma_i^2} .
\] The squared norm for the difference between the predictor
vectors \(\widetilde{\boldsymbol{\eta}}(\boldsymbol{v}_\alpha)\)
and \(\overline{\boldsymbol{\eta}}(\boldsymbol{u}_1)\),with
respect to this inner product, is defined as \[
\| \widetilde{\boldsymbol{\eta}}(\boldsymbol{v}_\alpha) -
\overline{\boldsymbol{\eta}}(\boldsymbol{u}_1)\|^2_V
=
\sum_i
\frac{|\widetilde{\boldsymbol{\eta}}_i(\boldsymbol{v}_\alpha)-\overline{\boldsymbol{\eta}}_i(\boldsymbol{u}_1)|^2}{\sigma_i^2}
.
\] Using this norm as the target loss function for the line
search avoids many potentially expensive evaluations of the true
posterior conditional log-density. We evaluate \(\widetilde{\boldsymbol{\eta}}_1=\widetilde{\boldsymbol{\eta}}(\boldsymbol{u}_1)\)
and make use of the linearised predictor information. Let \(\widetilde{\boldsymbol{\eta}}_\alpha=\widetilde{\boldsymbol{\eta}}(\boldsymbol{v}_\alpha)\)
and \(\overline{\boldsymbol{\eta}}_\alpha=\overline{\boldsymbol{\eta}}(\boldsymbol{v}_\alpha)=(1-\alpha)\widetilde{\boldsymbol{\eta}}(\boldsymbol{u}_0)+\alpha\overline{\boldsymbol{\eta}}(\boldsymbol{u}_1)\).
In other words, \(\alpha=0\)
corresponds to the previous linear predictor, and \(\alpha=1\) is the current estimate from
INLA. An exact line search would minimise \(\|\widetilde{\boldsymbol{\eta}}_\alpha-\overline{\boldsymbol{\eta}}_1\|\).
Instead, we define a quadratic approximation to the non-linear predictor
as a function of \(\alpha\), \[
\breve{\boldsymbol{\eta}}_\alpha =
\overline{\boldsymbol{\eta}}_\alpha + \alpha^2
(\widetilde{\boldsymbol{\eta}}_1 - \overline{\boldsymbol{\eta}}_1)
\] and minimise the quartic polynomial in \(\alpha\), \[
\begin{aligned}
\|\breve{\boldsymbol{\eta}}_\alpha-\overline{\boldsymbol{\eta}}_1\|^2
&=
\| (\alpha-1)(\overline{\boldsymbol{\eta}}_1 -
\overline{\boldsymbol{\eta}}_0) + \alpha^2
(\widetilde{\boldsymbol{\eta}}_1 - \overline{\boldsymbol{\eta}}_1) \|^2
.
\end{aligned}
\] If initial expansion and contraction steps are carried out,
leading to an initial guess of \(\alpha=\gamma^k\), where \(\gamma>1\) is a scaling factor (see
?bru_options, bru_method$factor) and \(k\) is the (signed) number of expansions
and contractions, the quadratic expression is replaced by \[
\begin{aligned}
\|\breve{\boldsymbol{\eta}}_\alpha-\overline{\boldsymbol{\eta}}_1\|^2
&=
\| (\alpha-1)(\overline{\boldsymbol{\eta}}_1 -
\overline{\boldsymbol{\eta}}_0) + \frac{\alpha^2}{\gamma^{2k}}
(\widetilde{\boldsymbol{\eta}}_{\gamma^k} -
\overline{\boldsymbol{\eta}}_{\gamma^k}) \|^2
,
\end{aligned}
\] which is minimised on the interval \(\alpha\in[\gamma^{k-1},\gamma^{k+1}]\).
Whereas the inlabru optimisation method leads to an estimate where \(\| \widetilde{\boldsymbol{\eta}} (\boldsymbol{u}_*) - \overline{\boldsymbol{\eta}}(\boldsymbol{u}_*)\|=0\) for a specific \(\boldsymbol{u}_*\), the overall posterior approximation accuracy depends on the degree of nonlinearity in the vicinity of \(\boldsymbol{u}_*\). There are two main options for evaluating this nonlinearity, using sampling from the approximate posterior distribution. The first option is \[ \begin{aligned} \sum_i \frac{E_{\boldsymbol{u}\sim \overline{p}(\boldsymbol{u}|\boldsymbol{y})}\left[ |\overline{\boldsymbol{\eta}}_i(\boldsymbol{u})-\widetilde{\boldsymbol{\eta}}_i(\boldsymbol{u})|^2\right]}{\mathrm{Var}_{\boldsymbol{u}\sim \overline{p}(\boldsymbol{u}|\boldsymbol{y})}(\overline{\boldsymbol{\eta}}_i(\boldsymbol{u}))} , \end{aligned} \] which is the posterior expectation of the component-wise variance-normalised squared deviation between the non-linear and linearised predictor. Note that the normalising variance includes the variability induced by the posterior uncertainty for \(\boldsymbol{\theta}\), whereas the \(\|\cdot\|_V\) norm used for the line search used only the posterior mode. Another option is \[ E_{(\boldsymbol{u},\boldsymbol{\theta})\sim \overline{p}(\boldsymbol{u},\boldsymbol{\theta}|\boldsymbol{y})} \left[\ln \frac{\overline{p}(\boldsymbol{u} |\boldsymbol{y},{\boldsymbol{\theta}})}{\widetilde{p}(\boldsymbol{u}|\boldsymbol{y},{\boldsymbol{\theta}})}\right] = E_{\boldsymbol{\theta}\sim \overline{p}(\boldsymbol{\theta}|\boldsymbol{y})} \left\{ E_{\boldsymbol{u}\sim \overline{p}(\boldsymbol{u}|\boldsymbol{y},\boldsymbol{\theta})} \left[\ln \frac{\overline{p}(\boldsymbol{u} |\boldsymbol{y},{\boldsymbol{\theta}})}{\widetilde{p}(\boldsymbol{u}|\boldsymbol{y},{\boldsymbol{\theta}})}\right] \right\} \] which is the Kullback–Leibler divergence for the conditional posterior densities, \(\mathsf{KL}\left(\overline{p}\,\middle\|\,\widetilde{p}\right)\), integrated over the approximate posterior distribution for \(\boldsymbol{\theta}\). Implementing this would require access to the likelihood and prior distribution details. The next section explores this in more detail.
We wish to assess how accurate the approximation is. Thus, we compare \(\widetilde{p}(\boldsymbol{u} | \boldsymbol{y}, \boldsymbol{\theta} )\) and \(\overline{p}(\boldsymbol{u} |\boldsymbol{y},\boldsymbol{\theta})\). With Bayes’ theorem, \[ \begin{aligned} p(\boldsymbol{u}|\boldsymbol{y},{\boldsymbol{\theta}}) &= \frac{p(\boldsymbol{u},\boldsymbol{y}|{\boldsymbol{\theta}})}{p(\boldsymbol{y}|{\boldsymbol{\theta}})} \\ &= \frac{p(\boldsymbol{y}|\boldsymbol{u},{\boldsymbol{\theta}}) p(\boldsymbol{u}|{\boldsymbol{\theta}})}{p(\boldsymbol{y}|{\boldsymbol{\theta}})}, \end{aligned} \] where \(p(\boldsymbol{u}|\boldsymbol{\theta})\) is a Gaussian density and \(p(\boldsymbol{y}|\boldsymbol{\theta})\) is a scaling factor that doesn’t depend on \(\boldsymbol{u}\). We can therefore focus on the behaviour of \(\ln p(\boldsymbol{y}|\boldsymbol{\theta},\boldsymbol{u})\) for the exact and linearised observation models.
Recall that the observation likelihood only depends on \(\boldsymbol{u}\) through \(\boldsymbol{\eta}\). Using a Taylor expansion with respect to \(\boldsymbol{\eta}\) and \(\boldsymbol{\eta}^*=\widetilde{\boldsymbol{\eta}}(\boldsymbol{u}_*)\), \[ \begin{aligned} \ln p(\boldsymbol{y}|\boldsymbol{\eta},\boldsymbol{\theta}) &= \ln p (\boldsymbol{y}|{\boldsymbol{\theta}},\boldsymbol{\eta}^*)) \\ &\qquad + \sum_i \left.\frac{\partial}{\partial\eta_i} \ln p (\boldsymbol{y} | {\boldsymbol{\theta}}, \boldsymbol{\eta}) \right|_{\boldsymbol{\eta}^*}\cdot (\eta_i - \eta^*_i) \\ &\qquad + \frac{1}{2}\sum_{i,j} \left.\frac{\partial^2}{\partial\eta_i\partial\eta_j} \ln p (\boldsymbol{y} | {\boldsymbol{\theta}}, \boldsymbol{\eta}) \right|_{\boldsymbol{\eta}^*}\cdot (\eta_i - \eta^*_i) (\eta_j - \eta^*_j) + \mathcal{O}(\|\boldsymbol{\eta}-\boldsymbol{\eta}^*\|^3), \end{aligned} \] Similarly, for each component of \(\widetilde{\boldsymbol{\eta}}\), \[ \begin{aligned} \widetilde{\eta}_i(\boldsymbol{u}) &= \eta^*_i +\left[\left.\nabla_{u}\widetilde{\eta}_i(\boldsymbol{u})\right|_{\boldsymbol{u}_*}\right]^\top (\boldsymbol{u} - \boldsymbol{u}_*) \\&\quad +\frac{1}{2}(\boldsymbol{u} - \boldsymbol{u}_*)^\top\left[\left.\nabla_{u}\nabla_{u}^\top\widetilde{\eta}_i(\boldsymbol{u})\right|_{\boldsymbol{u}_*}\right] (\boldsymbol{u} - \boldsymbol{u}_*) + \mathcal{O}(\|\boldsymbol{u}-\boldsymbol{u}^*\|^3) \\&= \eta_i^* + b_i(\boldsymbol{u}) + h_i(\boldsymbol{u}) + \mathcal{O}(\|\boldsymbol{u}-\boldsymbol{u}_*\|^3) \\&= \overline{\eta}_i(\boldsymbol{u}) + h_i(\boldsymbol{u}) + \mathcal{O}(\|\boldsymbol{u}-\boldsymbol{u}_*\|^3) \end{aligned} \]
where \(\nabla_u\nabla_u^\top\) is the Hessian with respect to \(\boldsymbol{u}\), \(b_i\) are linear in \(\boldsymbol{u}\), and \(h_i\) are quadratic in \(\boldsymbol{u}\). Combining the two expansions and taking the difference between the full and linearised log-likelihoods, we get \[ \begin{aligned} \ln \widetilde{p}(\boldsymbol{y}|\boldsymbol{u},\boldsymbol{\theta}) - \ln \overline{p}(\boldsymbol{y}|\boldsymbol{u},\boldsymbol{\theta}) &= \sum_i \left.\frac{\partial}{\partial\eta_i} \ln p (\boldsymbol{y} | {\boldsymbol{\theta}}, \boldsymbol{\eta}) \right|_{\boldsymbol{\eta}^*}\cdot h_i(\boldsymbol{u}) + \mathcal{O}(\|\boldsymbol{u}-\boldsymbol{u}_*\|^3) \end{aligned} \] Note that the log-likelihood Hessian difference contribution only involves third order \(\boldsymbol{u}\) terms and higher, so the expression above includes all terms up to second order.
Let \[
g_i^*=\left.\frac{\partial}{\partial\eta_i} \ln p (\boldsymbol{y} |
{\boldsymbol{\theta}}, \boldsymbol{\eta}) \right|_{\boldsymbol{\eta}^*}
\] and \[
\boldsymbol{H}^*_i =
\left.\nabla_{u}\nabla_{u}^\top\widetilde{\eta}_i(\boldsymbol{u})\right|_{\boldsymbol{u}_*}
.
\] and form the sum of their products, \(\boldsymbol{G}=\sum_i
g_i^*\boldsymbol{H}_i^*\). Then \[
\begin{aligned}
\ln \widetilde{p}(\boldsymbol{y}|\boldsymbol{u},\boldsymbol{\theta})
-
\ln \overline{p}(\boldsymbol{y}|\boldsymbol{u},\boldsymbol{\theta})
&=
\frac{1}{2}
\sum_i g_i^* (\boldsymbol{u}-\boldsymbol{u}_*)^\top
\boldsymbol{H}_i^* (\boldsymbol{u}-\boldsymbol{u}_*) +
\mathcal{O}(\|\boldsymbol{u}-\boldsymbol{u}_*\|^3)
\\&=
\frac{1}{2}
(\boldsymbol{u}-\boldsymbol{u}_*)^\top \boldsymbol{G}
(\boldsymbol{u}-\boldsymbol{u}_*) +
\mathcal{O}(\|\boldsymbol{u}-\boldsymbol{u}_*\|^3).
\end{aligned}
\] With \(\boldsymbol{m}=\mathsf{E}_\overline{p}(\boldsymbol{u}|\boldsymbol{y},\boldsymbol{\theta})\)
and \(\boldsymbol{Q}^{-1}=\mathsf{Cov}_\overline{p}(\boldsymbol{u},\boldsymbol{u}|\boldsymbol{y},\boldsymbol{\theta})\),
we obtain \[
\begin{aligned}
\mathsf{E}_{\overline{p}}\left[ \nabla_{\boldsymbol{u}} \left\{\ln
\widetilde{p}(\boldsymbol{y}|\boldsymbol{u},\boldsymbol{\theta}) -
\ln
\overline{p}(\boldsymbol{y}|\boldsymbol{u},\boldsymbol{\theta})\right\}\right]
&\approx
\boldsymbol{G}(\boldsymbol{m}-\boldsymbol{u}_*) ,
\\
\mathsf{E}_{\overline{p}}\left[
\nabla_{\boldsymbol{u}}\nabla_{\boldsymbol{u}}^\top \left\{\ln
\widetilde{p}(\boldsymbol{y}|\boldsymbol{u},\boldsymbol{\theta}) -
\ln
\overline{p}(\boldsymbol{y}|\boldsymbol{u},\boldsymbol{\theta})\right\}\right]
&\approx
\boldsymbol{G} ,
\\
\mathsf{E}_{\overline{p}}\left[ \ln
\widetilde{p}(\boldsymbol{y}|\boldsymbol{u},\boldsymbol{\theta}) -
\ln
\overline{p}(\boldsymbol{y}|\boldsymbol{u},\boldsymbol{\theta})\right]
&\approx
\frac{1}{2}
\mathop{\mathrm{tr}}(\boldsymbol{G}\boldsymbol{Q}^{-1}) +
\frac{1}{2}
(\boldsymbol{m}-\boldsymbol{u}_*)\boldsymbol{G}(\boldsymbol{m}-\boldsymbol{u}_*)^\top
.
\end{aligned}
\] For each \(\boldsymbol{\theta}\) configuration in the
INLA output, we can extract both \(\boldsymbol{m}\) and the sparse precision
matrix \(\boldsymbol{Q}\) for the
Gaussian approximation. The non-sparsity structure of \(\boldsymbol{G}\) is contained in the
non-sparsity of \(\boldsymbol{Q}\),
which allows the use of Takahashi recursion (inla.qinv(Q))
to compute the corresponding \(\boldsymbol{Q}^{-1}\) values needed to
evaluate the trace \(\mathop{\mathrm{tr}}(\boldsymbol{G}\boldsymbol{Q}^{-1})\).
Thus, to implement a numerical approximation of this error analysis only
needs special access to the log-likelihood derivatives \(g_i^*\), as \(H_i^*\) can in principle be evaluated
numerically.
For a given \(\boldsymbol{\theta}\), \[ \begin{aligned} \mathsf{KL}\left(\overline{p}\,\middle\|\,\widetilde{p}\right) &= E_{\overline{p}}\left[\ln\frac{\overline{p}(\boldsymbol{u}|\boldsymbol{y},\boldsymbol{\theta})}{\widetilde{p}(\boldsymbol{u}|\boldsymbol{y},\boldsymbol{\theta})}\right] \\&= E_{\overline{p}}\left[ \ln\frac{\overline{p}(\boldsymbol{y}|\boldsymbol{u},\boldsymbol{\theta})}{\widetilde{p}(\boldsymbol{y}|\boldsymbol{u},\boldsymbol{\theta})} \right] - \ln\frac{\overline{p}(\boldsymbol{y}|\boldsymbol{\theta})}{\widetilde{p}(\boldsymbol{y}|\boldsymbol{\theta})} . \end{aligned} \] The first term was approximated above. The second term can also be approximated using the derived quantities (to be continued…).
Summary: The form of the observation likelihood discrepancy shows that, given a linearised posterior \(\mathsf{N}(\boldsymbol{m},\boldsymbol{Q}^{-1})\), a Gaussian approximation to the nonlinear model posterior, \(\mathsf{N}(\widetilde{\boldsymbol{m}},\widetilde{\boldsymbol{Q}}^{-1})\), can be obtained from \(\widetilde{\boldsymbol{Q}}=\boldsymbol{Q}-\boldsymbol{G}\) and \(\widetilde{\boldsymbol{Q}}\widetilde{\boldsymbol{m}}=\boldsymbol{Q}\boldsymbol{m}-\boldsymbol{G}\boldsymbol{u}_*\). The K-L divergence becomes \[ \begin{aligned} \mathsf{KL}\left(\overline{p}\,\middle\|\,\widetilde{p}\right) &\approx \frac{1}{2} \left[ \ln\det(\boldsymbol{Q})- \ln\det(\boldsymbol{Q}-\boldsymbol{G}) -\mathop{\mathrm{tr}}\left(\boldsymbol{G}\boldsymbol{Q}^{-1}\right) + (\boldsymbol{m}-\boldsymbol{u}_*)^\top\boldsymbol{G}(\boldsymbol{Q}-\boldsymbol{G})^{-1}\boldsymbol{G}(\boldsymbol{m}-\boldsymbol{u}_*) \right] . \end{aligned} \] When the INLA posterior has mean \(\boldsymbol{m}=\boldsymbol{u}_*\), e.g. for models with additive Gaussian observation noise, and \(\boldsymbol{\theta}=\widehat{\boldsymbol{\theta}}_{\boldsymbol{u}_*}\), the last term vanishes.
Note: by implementing the K-L divergence accuracy metric, a by-product would be improved posterior estimates based on \(\widetilde{\boldsymbol{m}}\) and \(\widetilde{\boldsymbol{Q}}\).
On a side note, one might be concerned about initialisation at, or convergence to, a saddle point. Although it is not implemented in inlabru, we want to talk about the technicality how we define the initial linearisation point \(u_0\).
Generally speaking, any values of \(\boldsymbol{u}_0\) work except the case
that the gradient evaluated at \(\boldsymbol{u}_0\) is \(\boldsymbol{0}\) because the linearisation
point will never move away if the prior mean is also \(\boldsymbol{0}\). In general, this tends to
be a saddle point problem. In some cases the problem can be handled by
changing the predictor parameterisation or just changing the
initialisation point using the bru_initial option. However,
for true saddle point problems, it indicates that the predictor
parameterisation may lead to a multimodal posterior distribution or is
ill-posed in some other way. This is a more fundamental problem that
cannot be fixed by changing the initialisation point.
In these examples, where \(\beta\) and \(\boldsymbol{u}\) are latent Gaussian components, the predictors 1, 3, and 4 would typically be safe, but predictor 2 is fundamentally non-identifiable. \[ \begin{aligned} \boldsymbol{\eta}_1 &= \boldsymbol{u}, \\ \boldsymbol{\eta}_2 &= \beta \boldsymbol{u}, \\ \boldsymbol{\eta}_3 &= e^\beta \boldsymbol{u}, \\ \boldsymbol{\eta}_4 &= F_\beta^{-1} ( \Phi(z_\beta)) \boldsymbol{u}, \quad z_{\beta} \sim \mathsf{N}(0,1) . \end{aligned} \] Note that for \(\boldsymbol{\eta}_3\) and \(\boldsymbol{\eta}_4\), the partial derivatives with respect to \(\beta\) are zero for \(\boldsymbol{u}=\boldsymbol{0}\). However, the first inlabru iteration will give a non-zero estimate of \(\boldsymbol{u}\), so that subsequent iteration will involve both \(\beta\) and \(\boldsymbol{u}\).
Potential other choices for \(f(\cdot)\) include the posterior
expectation \(\overline{E}(\boldsymbol{u}|\boldsymbol{y})\)
and the marginal conditional modes, \[
\left\{\mathop{\mathrm{arg\,max}}_{u_i}
\overline{p}_{\boldsymbol{v}}(u_i|\boldsymbol{y}),\,i=1,\dots,n\right\},
\] which was used in inlabru up to version 2.1.15,
which caused problems for some nonlinear models. From version 2.2.3, the
joint conditional mode is used, \[
\mathop{\mathrm{arg\,max}}_{\boldsymbol{u}}
\overline{p}_{\boldsymbol{v}}(\boldsymbol{u}|\boldsymbol{y},\widehat{\boldsymbol{\theta}}_{\boldsymbol{v}}),
\] where \(\widehat{\boldsymbol{\theta}}=\mathop{\mathrm{arg\,max}}_{\boldsymbol{\theta}}
\overline{p}_{\boldsymbol{v}}(\boldsymbol{\theta}|\boldsymbol{y})\).↩︎
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