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A probabilistic programing library empowered with jax to speed up large simulations (still in progress!)

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Optimization-and-probabilistic-modeling

A CPU/GPU accelerated package is being developed for probablistic programming, Bayesian inference, linear and nonlinear optimization, with the capablity of vectorized computation for efficient execution in system biology studies, empowered by jax. (The project is still in progress)

[1] Chib, Siddhartha, and Edward Greenberg. "Understanding the metropolis-hastings algorithm." The american statistician 49.4 (1995): 327-335.

[2] Diwekar, Urmila M. Introduction to applied optimization. Vol. 22. Springer Nature, 2020.

[3] Boyd, Stephen, et al. "Distributed optimization and statistical learning via the alternating direction method of multipliers." Foundations and Trends® in Machine learning 3.1 (2011): 1-122.

[4] Foreman-Mackey, Daniel, et al. "emcee: the MCMC hammer." Publications of the Astronomical Society of the Pacific 125.925 (2013): 306.

[5] Bradbury, James, et al. "JAX: composable transformations of Python+ NumPy programs." Version 0.2 5 (2018): 14-24.

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A probabilistic programing library empowered with jax to speed up large simulations (still in progress!)

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