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Multi-output Matrix T Process


The multi-output matrix T regression model was first described by Conti and O' Hagan in their paper on Bayesian emulation of multi-output computer codes. It has been available in the dynaml.models.stp package of the dynaml-core module since v1.4.2.


The model starts from the multi-output gaussian process framework. The quantity of interest is some unknown function \mathbf{f}: \mathcal{X} \rightarrow \mathbb{R}^q, which maps inputs in \mathcal{X} (an arbitrary input space) to a q dimensional vector outputs.

\begin{align} \mathbf{f}(.)|B,\Sigma, \theta &\sim \mathcal{GP}(\mathbf{m}(.), c(.,.)\Sigma) \\ \mathbf{m}(x) &= B^\intercal \varphi(x) \end{align}

The input x is transformed through \varphi(.): \mathcal{X} \rightarrow \mathbb{R}^m which is a deterministic feature mapping which then calculates the inputs for a linear mean function \mathbf{m}(.). The parameters of this linear trend are contained in the matrix B \in \mathbb{R}^{m \times q} and \theta contains all the covariance function hyper-parameters.

The prior distribution of the multi-output function is represented as a matrix normal distribution, with c(.,.) representing the covariance between two input points, and the entries of \Sigma being the covariance between the output dimensions.

The predictive distribution when the output data D \in \mathbb{R}^{n\times q} is observed is calculated by first computing the conditional predictive distribution of \mathbf{f}(.) | D, \Sigma, B, \theta and then integrating this distribution with respect to the posterior distributions \Sigma|D and B|D.

The resulting predictive distribution \mathbf{f}(.)| \theta, D has the following structure.

\begin{align} \mathbf{f}(.)|\theta,D &\sim \mathcal{T}(\mathbf{m}^{**}(.), c^{**}\Sigma_{GLS};n-m) \\ \end{align}

The distribution is a matrix variate T distribution. It is described by

  • Mean \mathbf{m}^{**}(x).
  • Covariance between rows c^{**}(x_{1}, x_{2})
  • Covariance function between output columns \Sigma_{GLS}
  • Degrees of freedom n-m.
\begin{align} \mathbf{m}^{**}(x_{1}) &= B_{GLS}^{\intercal}\varphi(x_{1}) + (D-\varphi(X)B_{GLS})^{\intercal} C^{-1}c(x_{1},.)\\ c^{**}(x_{1}, x_{2}) &= \bar{c}(x_{1}, x_{2}) + \hat{c}(x_{1}, x_{2})\\ \bar{c}(x_{1}, x_{2}) &= c(x_{1}, x_{2}) - C(x_{1},.)^{\intercal}C^{-1}C(x_{2},.) \\ \hat{c}(x_{1}, x_{2}) &= H(x_{1})^{\intercal}.A^{-1}.H(x_{2})\\ H(x) &= (\varphi(x) - \varphi(X)C^{-1}c(x,.)) \\ A &= \varphi(X)^{\intercal}C^{-1}\varphi(X)\\ \end{align}

The matrices B_{GLS} = (\varphi(X)^{\intercal}C^{-1}\varphi(X))^{-1}\varphi(X)^{\intercal}C^{-1}D and \Sigma_{GLS} = (n-m)^{-1}(D - \varphi(X)B_{GLS})^{\intercal}C^{-1}(D - \varphi(X)B_{GLS}) are the generalized least squares estimators for the matrices B and \Sigma which we saw in the formulation above.

Multi-output Regression

An implementation of the multi-output matrix T model is available via the class MVStudentsTModel. Instantiating the model is very similar to other stochastic process models in DynaML i.e. by specifying the covariance structures on signal and noise, training data, etc.

//Obtain the data, some generic type
val trainingdata: DataType = _
val num_data_points: Int = _
val num_outputs:Int = _

val kernel: LocalScalarKernel[I] = _
val noiseKernel: LocalScalarKernel[I] = _
val feature_map: DataPipe[I, Double] = _

//Define how the data is converted to a compatible type
implicit val transform: DataPipe[DataType, Seq[(I, Double)]] = _

val model = MVStudentsTModel(
  kernel, noiseKernel, feature_map)(
  trainingData, num_data_points, num_outputs)