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Release Notes
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---
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title: Release notes 1.4.1
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tags: [getting_started,release_notes]
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keywords: release notes, announcements, what's new, new features
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last_updated: May 7, 2017
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summary: "Version 1.4.2 of DynaML, released May 7, 2017. Updates, improvements and new features."
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sidebar: mydoc_sidebar
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permalink: mydoc_release_notes_141.html
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folder: mydoc
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---
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## Core API
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### Additions
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**Package** `dynaml.models.neuralnets`
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- Added `GenericAutoEncoder[LayerP, I]`, the class `AutoEncoder` is now **deprecated**
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- Added `GenericNeuralStack[P, I, T]` as a base class for Neural Stack API
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- Added `LazyNeuralStack[P, I]` where the layers are lazily spawned.
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**Package** `dynaml.kernels`
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- Added `ScaledKernel[I]` representing kernels of scaled Gaussian Processes.
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**Package** `dynaml.models.bayes`
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- Added `*` method to `GaussianProcessPrior[I, M]` which creates a scaled Gaussian Process prior using the newly minted `ScaledKernel[I]` class
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- Added Kronecker product GP priors with the `CoRegGPPrior[I, J, M]` class
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**Package** `dynaml.models.stp`
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- Added multi-output Students' T Regression model of [Conti & O' Hagan](http://www.sciencedirect.com/science/article/pii/S0378375809002559) in class `MVStudentsTModel`
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**Package** `dynaml.probability.distributions`
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- Added `HasErrorBars[T]` generic trait representing distributions which can generate confidence intervals around their mean value.
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### Improvements
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**Package** `dynaml.probability`
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- Fixed issue with creation of `MeasurableFunction` instances from `RandomVariable` instances
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**Package** `dynaml.probability.distributions`
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- Changed error bar calculations and sampling of Students T distributions (vector and matrix) and Matrix Normal distribution.
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**Package** `dynaml.models.gp`
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- Added _Kronecker_ structure speed up to `energy` (marginal likelihood) calculation of multi-output GP models
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**Package** `dynaml.kernels`
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- Improved implicit paramterization of Matern Covariance classes
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**General**
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- Updated breeze version to latest.
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- Updated Ammonite version to latest

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