Chad Scherrer
cscherrer
Probabilistic programming in Rust and Julia
Languages
Top Repositories
Probabilistic programming via source rewriting
WIP successor to Soss.jl
A Julia implementation of Bayesian linear regression using marginal likelihood for hyperparameter optimization, as presented in Chris Bishop's book.
KeywordCalls makes it easy to define a method taking a NamedTuple considered as a an unordered collection of bound variables. The required redirection is done at compile time, so there's no runtime overhead.
Passage is a PArallel SAmpler GEnerator. The user specifies a hierarchical Bayesian model and data using the Passage EDSL, and Passage generates code to sample the posterior distribution in parallel.
SossMLJ makes it easy to build MLJ machines from user-defined models from the Soss probabilistic programming language
Repositories
127No description provided.
Probabilistic programming via source rewriting
No description provided.
A Julia implementation of Bayesian linear regression using marginal likelihood for hyperparameter optimization, as presented in Chris Bishop's book.
No description provided.
SossMLJ makes it easy to build MLJ machines from user-defined models from the Soss probabilistic programming language
Passage is a PArallel SAmpler GEnerator. The user specifies a hierarchical Bayesian model and data using the Passage EDSL, and Passage generates code to sample the posterior distribution in parallel.
No description provided.
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WIP successor to Soss.jl
Algebraic effects for Rust
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Julia package for automated Bayesian inference on a factor graph with reactive message passing
Julia package for automatic Bayesian inference on a factor graph with reactive message passing
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KeywordCalls makes it easy to define a method taking a NamedTuple considered as a an unordered collection of bound variables. The required redirection is done at compile time, so there's no runtime overhead.
A constant RNG, for cases when you need high efficiency and don't care about randomness
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Monoid, Functor, Applicative, Monad and more
Option, Try, Either, and some more common basic DataTypes
Implementing extensible effects in Julia
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Optimized implementations for higher-dimensional measures
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