Laplace Likelihood
The LaplaceLikelihood
is defined as
\[ p(y|f,\beta) = \frac{1}{2\beta}\exp\left(-\frac{|y-f|}{\beta}\right)\]
The augmentation
We use the technique developed in [1].
Running the inverse Laplace transform on the function $\exp(-\frac{\sqrt{x}}{\beta})$ returns the measure
\[\frac{e^{-\frac{1}{4\beta^2\omega}}}{2\beta\sqrt{\pi}}\omega^{-\frac{3}{2}},\]
which corresponds to an Inverse Gamma distribution with shape $\alpha=\frac{1}{2}$ and scale $\beta'=(2\beta)^{-2}$.
Therefore, we can write the Laplace distribution as the following Gaussian scale-mixture:
\[\operatorname{Laplace}(y|f,\beta) = \frac{\Gamma(\frac{1}{2})}{2\beta\sqrt{\pi}}\int_0^\infty \exp\left(-(y-f)^2\omega\right)\mathcal{IG}(\omega|\frac{1}{2},(2\beta)^{-2})d\omega,\]
where $\mathcal{IG}$ is the inverse Gamma distribution.
Conditional distributions (Sampling)
We are interested in the full-conditionals $p(f|y,\omega,\beta)$ and $p(\omega|y,f,\beta)$:
\[\begin{align*} p(f|y,\omega,\sigma,\nu) =& \mathcal{N}(f|\mu,\Sigma)\\ \Sigma =& \left(K^{-1} + \operatorname{Diagonal}(\omega^{-1})\right)^{-1}\\ \mu =& \Sigma\left(2\omega^{-1} y + K^{-1}\mu_0\right)\\ p(\omega_i|y_i,f_i,\sigma,\nu) =& \mathcal{IN}\left(\omega_i|\frac{1}{|2\beta(y-f)|}, 2(2\beta)^{-2}\right), \end{align*}\]
where $\mathcal{IN}$ is an Inverse Gaussian distribution.
Variational distributions (Variational Inference)
We define the variational distribution with a block mean-field approximation:
\[ q(f,\omega) = q(f)\prod_{i=1}^Nq(\omega_i) = \mathcal{N}(f|m,S)\prod_{i=1}^N \mathcal{IN}(\omega_i|\mu_i, \lambda_i).\]
The optimal variational parameters are given by:
\[\begin{align*} \mu_i =& \frac{1}{2\beta\sqrt{(y_i - \mu_i)^2 + S_{ii}}},\\ \lambda_i =& \frac{1}{2\beta^2}\\ m =& \Sigma\left(\mu y + K^{-1}\mu_0\right),\\ S =& \left(K^{-1} + \operatorname{Diagonal}(\mu)\right)^{-1}. \end{align*}\]
We get the ELBO as
\[ \mathcal{L} = N\left(\log \Gamma(\frac{1}{2}) - \log (\pi) - \log(2\beta)\right) + \sum_{i=1}^N -\left((y_i-m_i)^2 + S_{ii}\right)\theta_i - \operatorname{KL}(q(\omega)||p(\omega)) - \operatorname{KL}(q(f)||p(f)),\]
where $\theta_i = E_{q(\omega_i)}\left[\omega_i\right] = \mu_i$
\[\begin{align*} \operatorname{KL}(q(\omega_i|\mu_i,2\lambda)||p(\omega_i|\frac{1}{2},\lambda)) =& \frac{1}{2}\log 2\lambda - \frac{1}{2}\log 2\pi - \frac{1}{2}\log \lambda + \log \Gamma(\frac{1}{2}) + \frac{\lambda}{\mu}. \end{align*}\]