# Spatial derivatives of Gaussian process models

#### Collin Erickson

#### 2017-09-10

This vignette covers the derivatives of the surface created by a Gaussian process model with respect to the spatial dimensions. The other vignette has derivatives of the deviance (likelihood) with respect to the parameters.

# Gradient of mean function

The mean function is \[ \hat{y}(x) = \hat{\mu} + r^T R^{-1}(Y - 1\hat{\mu}). \] \(r\) is the only part that depends on \(x\), and is defined below, where \(K\) is the correlation function.

\[ r = (r_1(x), \ldots, r_d(x))^T\] \[ r_i(x) = K(z(x), z(u_i))\] \[\frac{\partial \hat{y}(x)}{\partial x_i} = \frac{\partial r}{\partial x_i}^T R^{-1}(Y - 1\hat{\mu})\]

\[\frac{\partial r_j}{\partial x_i} = \frac{\partial K(z(x), z(u_j))}{\partial x_i} \] This value depends on the correlation function \(K\). This will give the vector \(\frac{\partial r}{\partial x_i}\) which is calculates a single partial derivative, then the vector of these gives the gradient \(\nabla_x \hat{y}(x)\)

## Hessian of mean function

The second derivatives can be calculated similarly \[\frac{\partial^2 \hat{y}(x)}{\partial x_i \partial x_k} = \frac{\partial^2 r}{\partial x_i \partial x_k}^T R^{-1}(Y - 1\hat{\mu})\]

Each element of this matrix is \[\frac{\partial^2 r_j}{\partial x_i \partial x_k} = \frac{\partial^2 K(z(x), z(u_j))}{\partial x_i \partial x_k} \]

## Gaussian correlation derivative

The equations above work for any correlation function, but then we need to have the derivatives of the correlation function with respect to the spatial variables.

\[K(z(x), z(u_j)) = \exp(-\sum_{\ell =1}^d \theta_\ell (x_\ell - u_{j\ell})^2) \]

\[\frac{\partial K(z(x), z(u_j))}{\partial x_i} = -2\theta_i (x_i - u_{ji}) \exp(-\sum_{\ell =1}^d \theta_\ell (x_\ell - u_{j\ell})^2) \] The second derivative with respect to the same dimension is

\[\frac{\partial^2 K(z(x), z(u_j))}{\partial x_i^2} = (-2\theta_i + 4\theta_i^2 (x_i - u_{ji})^2) \exp(-\sum_{\ell =1}^d \theta_\ell (x_\ell - u_{j\ell})^2) \]

The cross derivative is \[\frac{\partial^2 K(z(x), z(u_j))}{\partial x_i \partial x_k} = 4\theta_i \theta_k (x_i - u_{ji}) (x_k - u_{jk}) \exp(-\sum_{\ell =1}^d \theta_\ell (x_\ell - u_{j\ell})^2) \]

## Gradient distribution

A big problem with using the gradient of the mean function of a GP is that it doesn’t give an idea of its distribution/randomness. The gradient could be zero in a region where the surface is not flat just because we don’t any information in that region yet.

Can we find the distribution of the gradient by taking a limit?

In \(d\) dimensions we

First start with the directional derivative in the direction of the ith component. \[ \frac{\partial \hat{y}(x)}{\partial x_i} = \lim_{\delta -> 0} \frac{ \hat{y}(x + \delta e_i) - \hat{y}(x)}{\delta} \]

Now we find the joint distribution of $(x + e_i) $ and $ (x)$

### Joint distribution

If \((y,z) \sim N((\mu_1, \mu_2), \Sigma)\) then \[ z|y \sim N(\mu_{z|y}, \Sigma_{z|y} )\] \[ \mu_{z|y}= \mu_2 + \Sigma_{zy}\Sigma_{yy}^{-1} (y - \mu_1)\] \[ \Sigma_{z|y} = \Sigma_{zz} - \Sigma_{zy}\Sigma_{yy}^{-1}\Sigma_{yz} \]

### Linear combination of normal variables is normal

\[ E[\hat{y}(x + \delta e_i) - \hat{y}(x)] = (\Sigma_{z_1y} - \Sigma_{z_2y})\Sigma_{yy}^{-1} (y - \mu_1) = (r_1-r_2)R^{-1}(y-\mu_1)\]

\[ E[\frac{\hat{y}(x + \delta e_i) - \hat{y}(x)}{\delta}] = (\frac{\Sigma_{z_1y} - \Sigma_{z_2y}}{\delta})\Sigma_{yy}^{-1} (y - \mu_1) = (\frac{r_1 - r_2}{\delta})R^{-1} (y - \mu_1)\]

\[ \frac{\Sigma_{z_1y} - \Sigma_{z_2y}}{\delta} = \frac{\hat{\sigma}^2 (r_1 - r_2)}{\delta}\] The jth element of this is \[ \lim_{\delta -> 0} \frac{R(x+\delta e_i, X_j) - R(x, X_j)}{\delta} = \frac{\partial R(x, X_j)}{\partial x_i} = \frac{\partial r_{(j)}}{\partial x_i}\] The mean of the gradient distribution is \[ \frac{\partial r}{\partial x_i}^TR^{-1} (y - \mu_1)\] This is the same as the derivative of the mean function.

The variance is the more difficult part.

### Variance of lincom

\[ Var\big[ \frac{\hat{y}(x + \delta e_i) - \hat{y}(x)}{\delta} \big] = \frac{1}{\delta^2}Var\big[ \hat{y}(x + \delta e_i) - \hat{y}(x) \big]\] \[=\frac{1}{\delta^2}\big( Var\big[ \hat{y}(x + \delta e_i) \big] + Var\big[ \hat{y}(x) \big] + -2 Cov\big[ \hat{y}(x + \delta e_i), \hat{y}(x) \big] \big)\] \[ Var\big[ \hat{y}(x + \delta e_i) |Y \big] = \Sigma_{11} - \Sigma_{1Y} \Sigma_{YY}^{-1} \Sigma_{Y1} \] \[ Var\big[ \hat{y}(x) |Y \big] = \Sigma_{22} - \Sigma_{2Y} \Sigma_{YY}^{-1} \Sigma_{Y2} \] \[ Cov\big[ \hat{y}(x + \delta e_i), \hat{y}(x)|Y \big] = Cov\big[ \hat{y}(x + \delta e_i), \hat{y}(x) \big] - Cov\big[ \hat{y}(x + \delta e_i), Y \big] \Sigma_{YY}^{-1} Cov\big[ Y, \hat{y}(x) \big] \] \[ = \Sigma_{12} - \Sigma_{1Y} \Sigma_{YY}^{-1} \Sigma_{Y2}\] \[ = \hat{\sigma}^2 \big(R\big[ \hat{y}(x + \delta e_i), \hat{y}(x) \big] + r_1 R^{-1} r_2 \big) \]

\[=\frac{1}{\delta^2}\big( \Sigma_{11} - \Sigma_{1Y} \Sigma_{YY}^{-1} \Sigma_{Y1} + \Sigma_{22} - \Sigma_{2Y} \Sigma_{YY}^{-1} \Sigma_{Y2} + -2 (\Sigma_{12} - \Sigma_{1Y} \Sigma_{YY}^{-1} \Sigma_{Y2}) \] \[=\frac{1}{\delta^2}\big( \Sigma_{11} + \Sigma_{22} -2 \Sigma_{12} +2 (\Sigma_{1Y}- \Sigma_{2Y}) \Sigma_{YY}^{-1} ( \Sigma_{1Y}-\Sigma_{Y2}) \]

CONTINUE HERE USE THIS AS GUIDE http://mlg.eng.cam.ac.uk/mchutchon/DifferentiatingGPs.pdf

\[=\frac{1}{\delta^2}\big( Var\big[ \hat{y}(x + \delta e_i) \big] + Var\big[ \hat{y}(x) \big] + -2 Cov\big[ \hat{y}(x + \delta e_i), \hat{y}(x) \big] \big)\]

I could have just used this formula: \[ Var[(1,-1)^T (z_1, z_2)] = (1,-1)^T Cov((z_1, z_2)) (1,-1)\]

\[ \frac{\partial R(z_1, z_2)}{\partial \delta} = \]