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Lecture 19

7.1. Lecture 19#

Gaussian process demo notebook#

Let’s step through the Gaussian processes demonstration notebook.

  • A stochastic process is a collection of random variables (RVs) indexed by time or space. I.e., at each time or at each space point there is a random variable.

  • A Gaussian process (GP) is a stochastic process with definite relationships (correlations!) between the RVs. In particular, any finite subset (say at \(x_1\), \(x_2\), \(x_3\)) has a multivariate normal distribution.

    • cf. the definitions at the beginning of the notebook

    • A GP is the natural generalization of multivariate random variables to infinite (countably or continous) index sets.

    • They look like random functions, but with characteristic degrees of smoothness, correlation lengths, and ranges.

  • A multivariate Gaussian distribution in general is

    \[ p(\xvec|\muvec,\Sigma) = \frac{1}{\sqrt{\det(2\pi\Sigma)}} e^{-\frac{1}{2}(\xvec-\muvec)^\intercal\Sigma^{-1}(\xvec - \muvec)} \]

    For the bivariate case:

    \[\begin{split} \muvec = \pmatrix{\mu_x\\ \mu_y} \quad\mbox{and}\quad \Sigma = \pmatrix{\sigma_x^2 & \rho \sigma_x\sigma_y \\ \rho\sigma_x\sigma_y & \sigma_y^2} \quad\mbox{with}\ 0 < \rho^2 < 1 \end{split}\]

    and \(\Sigma\) is positive definite. In the notebook the case \(\sigma_x = \sigma_y = \sigma\) is seen to be an ellipse.

  • Think of the bivariate case with strong correlations (\(|\rho|\) close to one) as belong to two points close together \(\Lra\) the smoothness of the function tells us that the lines in the plot in the notebook should be closer to flat (small slope).

  • Kernels: these are the covariance functions that, given two points in the \(N\)-dimensional space, say \(\xvec_1\) and \(\xvec_2\), return the covariance between \(\xvec_1\) and \(\xvec_2\).

  • Consider these vectors to be one-dimensional for simplicity, so we have \(x_1\) and \(x_2\). The

    \[ K_{\rm KBF}(x_1,x_2) = \sigma^2 e^{-(x_1-x_2)^2/2\ell^2} \]

    Compare this to

    \[\begin{split} \Sigma = \sigma^2 \pmatrix{1 & \rho \\ \rho & 1} . \end{split}\]

    The diagonals have \(x_1 = x_2\) while \(\rho = e^{-(x_1-x_2)^2/2\ell^2}\).

    • So when \(x_1\) and \(x_2\) are close compared to \(\ell\) then the values of the sample at \(x_1\) and \(x_2\) are highly correlated.

    • When \(x_1\) and \(x_2\) are far apart, \(\rho \rightarrow 0\) and they become independent. So \(\ell\) plays the role of a correlation length.

  • Look at the examples for different \(l\). What does the RBF Cov. Matrix plot show? This is the covariance matrix!

  • GP models for regression

    • First example of using GPs \(\Lra\) train on some points, predict elsewhere *with$ error bands.

  • No-core shell model \(\hbar\omega\) dependence.

Games with Gaussian process websites#

Here are four websites with Gaussian process visualizations and some things to try with each.

  1. The Sample Size Calculations for Computer Experiments app provides a sandbox for playing with Gaussian processes. Read the “About” tab first; it includes definitions of parameters used in the GP “correlation family” (which define the covariance). The “Sample Path Plots” tab has an app that lets you draw samples of functions that depend on user selected parameters (actually “hyperparameters”) that specify the details of the correlation family.

    • Start with Sample Path Plots.

    • Try changing Correlation length (switch “No. of realizations” to get new draws).

    • Change “Select correlation family”.

      • Note the extra parameter with Matern.

    • See the “About” tab for formulas.

  2. The Gaussian process regression: a function space perspective app by Pradeep Ranganathan “demonstrates how a GP prior is a distribution over functions, and how observing data conditions the prior to obtain the GP posterior.”

    • You should see successive draws from a GP.

    • Try changing the covariance and length scale; does it change as predicted?

    • Now try adding points. What happens?

    • Try adjusting the noise. What happens?

  3. The Gaussian process regression demo app “demonstrates Gaussian process regression with one covariate and a set of different covariance kernels.”

    • Check “Show mean and credible intervals” and “sample independently”.

    • Add observations.

    • Add a new process.

  4. A visual exploration of Gaussian processes is a complete run-through of Gaussian processes.

    • Try changing the covariance matrix under Multivariate Gaussian distributions.

    • Try the different kernels under Gaussian Processes / Kernels.

    • Under Gaussian Processes, try Prior distribution and Posterior distribution.

    • Try Gaussian Processes / Combining different kernels.

Brief introduction to GPs from Melendez et al.#

Here we use the paper by Melendez et al., Phys. Rev. C 100, 044001 (2019), arXiv:1904.10581 as an introduction to the practical use of GPs.

  • Gaussian processes (GPs) are often used for nonparametric regression. Cf. fitting a polynomial, where the basis functions are \(1, x, x^2,\ldots, x^d\) and the coefficients are the parameters. This is parametric regression. So with GPs we do not have the corresponding set of parameters for basis functions. But there are parameters that specify the GP itself.

  • Besides regression, the other most common application of GPs are to interpolation. To carry out either of these we need to calibrate, which means fitting the GP parameters.

  • A GP is specified by a mean function \(m(x)\) [often written \(\mu(x)\)] and a positive semidefinite covariance function, also called a kernel, \(\kappa(x,x')\) where \(x \in \mathbb{R}^d\) and \(d\) is the dimension of the parameter space. (We will generally suppress the \(d\) dependence of \(\kappa\).)

    • If we know \(m(x)\) and \(\kappa(x,x')\), then a GP \(f(x)\) is denoted

      \[ f(x) \sim \mathcal{GP}[m(x), \kappa(x,x')] \]

      in the same way a normal distribution is \(g(x) \sim \mathcal{N( \mu,\sigma^2)}\).

  • While the definition has continuous, infinite-dimensional \(x\), in practice we use a finite number of points:

    \[ \xvec = \{x_i\}_{i=1}^N \quad\mbox{and}\quad \fvec = \{f(x_i)\}_{i=1}^N \]

    where \(N\) is the number of input “points” (which are actually vectors in general). Defining

    \[ \mvec = m(\xvec) \in \mathbb{R}^N \quad\mbox{and}\quad K = \kappa(\xvec,\xvec') \in \mathbb{R}^{N\times N} \]

    we can write that any subset of inputs form a multivariate Gaussian:

    \[ \Lra \fvec | \xvec \sim \mathcal{N}(\mvec,K) \]

    which can serve as the definition of a GP. We say that “\(\fvec\) is conditional on \(\xvec\)”.

    • The mean function is the a priori “best guess” for \(f\). If there are no features, this is often taken to be zero.

    • Our specification of the kernel tells us what \(K\) is.

  • So how do we use this GP? Let’s assume we already know \(\thetavec\), the set of hyperparameters. And suppose we know the value of the function \(f\) at a set of \(\xvec_1\) points \(\Lra\) this is our training set.

    • Therefore partition the inputs into \(N_1\) training an \(N_2\) test points (the latter are our predictions):

    \[ \xvec = [\xvec_1 \xvec_2]^\intercal \quad\mbox{and}\quad \fvec = f(\xvec) = [\fvec_1 \fvec_2]^\intercal . \]

    • There are corresponding vectors \(\mvec_1\) and \(\mvec_2\) and covariance matrices.

  • The definition of a GP says the joint distribution of \(\fvec_1\), \(\fvec_2\) is

    \[\begin{split} \pmatrix{\fvec_1 \\ \fvec_2} | \xvec,\thetavec \sim \mathcal{N}\biggl[\pmatrix{m_1 \\ m_2}, \pmatrix{ K_{11} & K_{12} \\ K_{21} & K_{22}} \biggr] \end{split}\]

    where

    \[\begin{split}\begin{align} K_{11} & = \kappa(\xvec_1, \xvec_1) \\ K_{22} & = \kappa(\xvec_2, \xvec_2) \\ K_{12} & = \kappa(\xvec_1, \xvec_2) = K_{21}^\intercal \\ \end{align}\end{split}\]

    • Then manipulation of matrices tells us that

      \[ \fvec_2 | \xvec_1, \fvec_1, \thetavec \sim \mathcal{N}(\tilde m_2, \widetilde K_{22}) \]

      where

      \[\begin{split}\begin{align} \widetilde m_2 & = m_2 + K_{21}K_{11}^{-1}(\fvec_1 - \mvec_1) \\ \widetilde K_{22} & \equiv K_{22} - K_{21}K_{11}^{-1}K_{12} . \end{align}\end{split}\]

      So \(\tilde m_2\) is our best prediction (solid line) and \(\widetilde K_{22}\) is the variance (determines the band).

      This makes sense:

      \[ p(\fvec_2,\fvec_1) = p(\fvec_2|\fvec_1) \times p(\fvec_1) \quad\Lra\quad p(\fvec_2|\fvec_1) = p(\fvec_2,\fvec_1) / p(\fvec_1) \]

      and the ratio of Gaussians is also a Gaussian \(\Lra\) we only need to figure out the matrices!

  • We can make draws from \(\mathcal{N}(\tilde m_2, \widetilde K_{22})\) and plot. With a dense enough grid, we will have lines that go through the \(\xvec_1\) points and have bands in between, which grow larger when further from points.

  • If we have Gaussian white noise at each point, then \(K_{11} \rightarrow K_{11} + \sigma_n^2 I_{N_1}\) (if the variance of the noise is the same; otherwise it is a diagonal matrix with entries given by the separate variances).

    • We need to add some noise (called adding a “nugget”) even if the data is perfect for numerical reasons: \(K_{11}^{-1}\) will very probably be unstable without it.

    • For predictions with noise, then \(K_{22} \rightarrow K_{22} + \sigma_n^2 I_{N_2}\).

    Aside: how do we make draws from a multivariate Gaussian (normal) distribution if we know how to make draws from \(\mathcal{N}(0,1)\)?

    • Suppose \(\xvec \sim \mathcal{N}(\muvec, \Sigmavec)\), where \(\xvec\) and \(\muvec\) are dimension \(d\) and \(\Sigmavec\) is \(d\times d\). Then we want to make draws with the pdf

      \[ p(\xvec|\muvec,\Sigmavec) = \frac{1}{\sqrt{\det 2\pi\Sigmavec}} e^{-\frac{1}{2}(\xvec-\muvec)^\intercal \Sigmavec^{-1}(\xvec - \muvec)} . \]

    • The procedure is:

      • Generate \(\uvec \sim \mathcal{N}(0,I_d)\) from \(d\) independent draws of \(\mathcal{N}(0,1)\). This yields \(\uvec = (u_0, u_1, \ldots, u_{d-1})^\intercal\).

      • Find \(L\) such that \(\Sigmavec = L L^\intercal\) (using a Cholesky decomposition).

      • Then \(\xvec = \muvec + L\uvec\) has the desired distribution.

    • Check: What is the expectation value of \(\xvec\), given \(E[\uvec] = 0\)?

      \[ E[\xvec] = E[\muvec + L\uvec] = \mu + L E[\uvec] = \muvec \]

      which is the correct mean. Note that these manipulations used that expectation values are linear.

    • Now try for \(\Sigmavec\):

      \[\begin{split}\begin{align} \Sigmavec &\overset{?}{=} E[(\xvec - \muvec)(\xvec^\intercal - \mu^\intercal)] = E[(L\uvec)(L\uvec)^T] \\ &= E[L\uvec\uvec^\intercal L^\intercal] = L E[\uvec\uvec^\intercal] = L I_d L^\intercal = \Sigmavec \end{align}\end{split}\]

      so it works!

  • Let’s check that it works for the special case of one training, one test point.

    • Let’s also take mean zero to avoid clutter.

    • Then

      \[\begin{split} \pmatrix{f_1\\ f_2} \sim \mathcal{N}\Bigl[\pmatrix{0\\ 0}, \pmatrix{\Sigma_{11} & \Sigma_{12} \\ \Sigma_{21} & \Sigma_{22}} \Bigr] \end{split}\]

      where

      \[\begin{split} \Sigmavec = \pmatrix{\sigma_1^2 & \rho\sigma_1\sigma_2 \\ \rho\sigma_1\sigma_2 & \sigma_2^2} \end{split}\]

    • The claim is that

      \[ f_2 | f_1 \sim \mathcal{N}(\Sigma_{21}\Sigma_{11}^{-1}f_1, \Sigma_{22} - \Sigma_{21}\Sigma_{11}^{-1}\Sigma_{12}) \]

      i.e., that it is a Gaussian distribution with mean \(\Sigma_{21}\Sigma_{11}^{-1}f_1\) and variance \(\Sigma_{22} - \Sigma_{21}\Sigma_{11}^{-1}\Sigma_{12}\).

    • Try it explicitly:

      \[\begin{split} p(f_2|f_1) = p(f_1,f_2)/p(f_1) = \frac{\frac{1}{\sqrt{\det(2\pi\Sigmavec)}}e^{-(f_1 \ f_2)\,\Sigmavec^{-1}\pmatrix{f_1 \\ f_2}}} {\frac{1}{\sqrt{2\pi\Sigma_{11})}}e^{-f_1^2\Sigma_{11}^{-1}}} \end{split}\]

      All of the \(\Sigma_{ij}\)’s are just scalars now and \(\Sigma_{12} = \Sigma_{21}\).

      \[\begin{split} \Sigmavec^{-1} = \frac{1}{\det\Sigmavec}\pmatrix{\Sigma_{22}& -\Sigma_{21} \\ -\Sigma_{12} & \Sigma_{11}} = \frac{1}{\Sigma_{11}\Sigma_{22}-\Sigma_{12}\Sigma_{21}}\pmatrix{\Sigma_{22}& -\Sigma_{21} \\ -\Sigma_{12} & \Sigma_{11}} \end{split}\]

      which yields

      \[\begin{split} (f_1 \ f_2)\,\Sigmavec^{-1}\pmatrix{f_1 \\ f_2} = \frac{(-\Sigma_{22}f_1^2 + 2\Sigma_{21}f_1 f_2 - \Sigma_{11}f_2^2)}{\det\Sigmavec} . \end{split}\]

      So if we gather the \(\Sigma_{22}\) parts, we find to complete the square we need \((f_2 - \Sigma_{21}\Sigma_{11}^{-1}f_1)\), which identifies the mean, and then the variance part comes out as advertised.

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