# v1.4

Version 1.4 of DynaML, released Sept 23, 2016, implements a number of new models (multi-output GP, student T process, random variables, etc) and features (Variance control for CSA, etc).

## Models¶

The following inference models have been added.

### Meta Models & Ensembles¶

• LSSVM committees.

### Stochastic Processes¶

• Multi-output, multi-task Gaussian Process models as reviewed in Lawrence et. al.
• Student T Processes: single and multi output inspired from Shah, Ghahramani et. al
• Performance improvement to computation of marginal likelihood and posterior predictive distribution in Gaussian Process models.
• Posterior predictive distribution outputted by the AbstractGPRegression base class is now changed to MultGaussianRV which is added to the dynaml.probability package.

## Kernels¶

• Added StationaryKernel and LocallyStationaryKernel classes in the kernel APIs, converted RBFKernel, CauchyKernel, RationalQuadraticKernel & LaplacianKernel to subclasses of StationaryKernel

• Added MLPKernel which implements the maximum likelihood perceptron kernel as shown here.

• Added co-regionalization kernels which are used in Lawrence et. al to formulate kernels for vector valued functions. In this category the following co-regionalization kernels were implemented.

• CoRegRBFKernel
• CoRegCauchyKernel
• CoRegLaplaceKernel
• CoRegDiracKernel

• Improved performance when calculating kernel matrices for composite kernels.

• Added :* operator to kernels so that one can create separable kernels used in co-regionalization models.

## Optimization¶

• Improved performance of CoupledSimulatedAnnealing, enabled use of 4 variants of Coupled Simulated Annealing, adding the ability to set annealing schedule using so called variance control scheme as outlined in de-Souza, Suykens et. al.

## Pipes¶

• Added Scaler and ReversibleScaler traits to represent transformations which input and output into the same domain set, these traits are extensions of DataPipe.

• Added Discrete Wavelet Transform based on the Haar wavelet.