BayesInference 0.0.11

GNU GENERAL PUBLIC LICENSE

Bayesian Inference (BI)

BI software is available in both Python and R. It aims to unify the modeling experience by integrating an intuitive model-building syntax with the flexibility of low-level abstraction coding available but also pre-build function for high-level of abstraction and including hardware-accelerated computation for improved scalability.

Currently, the package provides:

• Data manipulation:

  • One-hot encoding
  • Conversion of index variables
  • Scaling

• Models (Using Numpyro):

  • Linear Regression for continuous variable
  • Multiple continuous Variable
  • Interaction between variables
  • Categorical variable
  • Binomial model
  • Beta binomial
  • Poisson model
  • Gamma-Poisson
  • Multinomial
  • Dirichlet model
  • Zero inflated
  • Varying intercept
  • Varying slopes
  • Gaussian processes
  • Measuring error
  • Latent variable]
  • PCA
  • GMM
  • DPMM
  • Network model
  • Network with block model
  • Network control for data collection biases
  • BNN

• Model diagnostics (using ARVIZ):

  • Data frame with summary statistics
  • Plot posterior densities
  • Bar plot of the autocorrelation function (ACF) for a sequence of data
  • Plot rank order statistics of chains
  • Forest plot to compare HDI intervals from a number of distributions
  • Compute the widely applicable information criterion
  • Compare models based on their expected log pointwise predictive density (ELPD)
  • Compute estimate of rank normalized split-R-hat for a set of traces
  • Calculate estimate of the effective sample size (ESS)
  • Pair plot
  • Density plot
  • ESS evolution plot

Why?

1. To learn

2. Easy Model Building:

The following linear regression model (rethinking 4.Geocentric Models): $$ \text{height} \sim \mathrm{Normal}(\mu,\sigma) $$

$$ \mu = \alpha + \beta \cdot \text{weight} $$

$$ \alpha \sim \mathrm{Normal}(178,20) $$

$$ \beta \sim \mathrm{Normal}(0,10) $$

$$ \sigma \sim \mathrm{Uniform}(0,50) $$

can be declared in the package as

from BI import bi

# Setup device------------------------------------------------
m = bi(platform='cpu')

# Import Data & Data Manipulation ------------------------------------------------
# Import
from importlib.resources import files
data_path = files('BI.resources.data') / 'Howell1.csv'
m.data(data_path, sep=';') 
m.df = m.df[m.df.age > 18] # Manipulate
m.scale(['weight']) # Scale

# Define model ------------------------------------------------
def model(weight, height):    
    a = m.dist.normal(178, 20, name = 'a') 
    b = m.dist.lognormal(0, 1, name = 'b') 
    s = m.dist.uniform(0, 50, name = 's') 
    m.normal(a + b * weight , s, obs = height) 

# Run mcmc ------------------------------------------------
m.fit(model)  # Optimize model parameters through MCMC sampling

# Summary ------------------------------------------------
m.summary() # Get posterior distributions

Todo

1. GUI
2. Documentation
3. Implementation of additional MCMC sampling methods