Bayesian Inference
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Load the BI module from Python
Run a starting test for the BI environment
Asymmetric Laplace Quantile Distribution
Asymmetric Laplace distribution
Sample from a Bernoulli distribution.
BetaBinomial
Samples from a Beta-Proportion distribution.
Beta Distribution
Samples from a Binomial distribution.
Conditional Autoregressive (CAR) Distribution
Sample from a Categorical distribution.
Cauchy Distribution
Samples from a Chi-squared distribution.
The Delta distribution.
Samples from a Dirichlet Multinomial distribution.
Samples from a Dirichlet distribution.
Samples from a Discrete Uniform distribution.
Euler-Maruyama method
Samples from an Exponential distribution.
Gamma-Poisson Distribution
Gamma Distribution
Gaussian Copula Beta distribution.
Gaussian Copula Distribution
Samples from a Gaussian Random Walk distribution.
Gaussian State Space Distribution
Samples from a Geometric distribution.
Gompertz Distribution
Samples from a Gumbel (or Extreme Value) distribution.
HalfCauchy Distribution
Samples from a HalfNormal distribution.
InverseGamma Distribution
Kumaraswamy Distribution
Laplace Distribution
Samples from a left-truncated distribution.
Levy distribution
LKJ Cholesky Distribution
Samples from an LKJ (Lewandowski, Kurowicka, Joe) distribution for cor...
Log Normal distribution
Samples from a Log Uniform distribution.
Samples from a Logistic distribution.
Low Rank Multivariate Normal Distribution
Lower Truncated Power Law Distribution
Matrix Normal Distribution
A finite mixture of component distributions from different families.
A finite mixture of component distributions from the same family.
A marginalized finite mixture of component distributions.
Multinomial logit
Samples from a Multinomial distribution.
Multinomial distribution.
Samples from a Multivariate Normal distribution.
Multivariate Student's t Distribution
Samples from a Negative Binomial Logits distribution.
Sample from a Negative Binomial distribution with probabilities.
Samples from a Negative Binomial distribution.
Samples from a Normal (Gaussian) distribution.
Ordered Logistic Distribution
Samples from a Pareto distribution.
Poisson Distribution
Samples from a Projected Normal distribution.
Relaxed Bernoulli Logits Distribution.
Samples from a Relaxed Bernoulli distribution.
Samples from a right-truncated distribution.
Samples from a Soft Laplace distribution.
Student's t-distribution.
Truncated Cauchy Distribution
Truncated Distribution
Truncated Normal Distribution
Truncated PolyaGamma Distribution
Two-Sided Truncated Distribution
Uniform Distribution
Samples from a Weibull distribution.
Wishart Cholesky Distribution
Wishart distribution for covariance matrices.
Generic Zero Inflated distribution.
Zero-Inflated Negative Binomial Distribution
A Zero Inflated Poisson distribution.
zero_sum_normal
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Check if the default virtual environment is available
Convert Posterior Samples
Package load hook
Package attach
Import the BI Python Module
Install dependencies
List packages in a virtual environment
Package Load
Remove a virtual environment
Create a Python virtual environment
Update the BI module from Python
Beta version of 'Bayesian Inference' (BI) using 'python' and BI. It aims to unify the modeling experience by providing an intuitive model-building syntax together with the flexibility of low-level abstraction coding. It also includes pre-built functions for high-level abstraction and supports hardware-accelerated computation for improved scalability, including parallelization, vectorization, and execution on CPU, GPU, or TPU.