jaxampler 0.0.3

JAX based lib for sampling statistical distributions.

JAXampler

JAX based lib for sampling statistical distributions.

Install

pip install jaxampler

Samplers

To sample from a distribution, import the corresponding sampler from the jaxampler.sampler module and call the sample method with the required arguments.

from jaxampler.rvs import Beta, Normal
from jaxampler.sampler import AcceptRejectSampler

scale = 1.35
N = 100_000

target_rv = Normal(mu=0.5, sigma=0.2)
proposal_rv = Beta(alpha=2, beta=2)

ar_sampler = AcceptRejectSampler()

samples = ar_sampler.sample(
    target_rv=target_rv,
    proposal_rv=proposal_rv,
    scale=scale,
    N=N,
)

JAXampler currently supports the following samplers:

• Inverse Transform Sampling
• Accept-Rejection Sampling
• Adaptive Accept-Rejection Sampling
• Metropolis-Hastings
• Hamiltonian Monte Carlo
• Slice Sampling
• Gibbs Sampling
• Importance Sampling

Random Variables

To create a new random variable, import the corresponding type from the jaxampler.rvs i.e. DiscreteRV and ContinuousRV for discrete and continuous random variables respectively. Then, instantiate the random variable with the required parameters and implement the necessary methods (logpdf, cdf, and ppf etc). JAXampler currently supports the following random variables:

Discrete Random Variables

• Bernoulli
• Binomial
• Geometric
• Poisson
• Rademacher

Continuous Random Variables

• Beta
• Boltzmann
• Cauchy
• Chi Square
• Exponential
• Gamma
• Gumbel
• Laplace
• Log Normal
• Logistic
• Multivariate Normal
• Normal
• Pareto
• Rayleigh
• Student t
• Triangular
• Truncated Normal
• Truncated Power Law
• Uniform
• Weibull

Citing Jaxampler

To cite this repository:

@software{jaxampler2023github,
    author  = {Meesum Qazalbash},
    title   = {{JAXampler}: tool for sampling statistical distributions},
    url     = {http://github.com/Qazalbash/jaxampler},
    version = {0.0.3},
    year    = {2023}
}

Contributors

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