https://github.com/GPflow/GPflow
Revision 6baeb43b1d68518e6ba0074e702d014f0fe85865 authored by jch5f on 15 June 2018, 10:31:27 UTC, committed by James Hensman on 15 June 2018, 10:31:27 UTC
* Add scaling to studentT conditional variance The conditional variance of the Student’s T distributions is proportional to the square of the scale of the distribution. See https://en.wikipedia.org/wiki/Student%27s_t-distribution#In_terms_of_sca ling_parameter_σ,_or_σ2. I’ve incorporated the correct scaling factor. * explicit scale dtype and tensor broadcasting Added an explicit data type for the Student’s T scale parameter, and made the broadcasting in the conditional_variance method explicit.
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Tip revision: 6baeb43b1d68518e6ba0074e702d014f0fe85865 authored by jch5f on 15 June 2018, 10:31:27 UTC
Likelihood/students t variance scaling (#777)
Likelihood/students t variance scaling (#777)
Tip revision: 6baeb43
roadmap.md
This document covers major planned development items for GPflow.
# Computational speed
- Add further benchmarks to [benchmark repository](https://github.com/GPflow/GPflowBenchmarks) including multiple GPUs.
- Incorporate remaining Tom Nickson GPU code from [branch](https://github.com/c0g/tomserflow) into TensorFlow main.
# Features
- Add ability to exploit Kronecker structure.
# Housekeeping
- See also issues marked "enhancement" in the GitHub [list](https://github.com/GPflow/GPflow/issues).
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