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License: GNU General Public License v2.0
Python implementation of Markov Jump Hamiltonian Monte Carlo
License: GNU General Public License v2.0
The theano implementation currently barfs when run on a CPU. I think this is just happening because we're currently hard coding float precision - there is probably a way to dynamically set this
This looks really neat. What do you think of adding this sampler to pymc3 where it would be directly usable in a probabilistic programming framework?
It's pretty easy to add new samplers, for example: https://github.com/pymc-devs/pymc3/blob/master/pymc3/step_methods/nuts.py#L14
Some major refactoring of the package structure is necessary.
A big issue is that the current import configuration is slow as it loads all modules in the package if you try to import any one module.
I think that something like the numpy package structure is desirable - or maybe with aggressive refactoring we can package this as a barebones plug-and-play module?
What do we want this to be?
A declarative, efficient, and flexible JavaScript library for building user interfaces.
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Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
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