# 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
Raw data
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"description": "# Bayesian Inference (BI) \n 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.\n\nCurrently, the package provides:\n\n+ Data manipulation:\n + One-hot encoding\n + Conversion of index variables\n + Scaling\n \n+ Models (Using Numpyro):\n \n + Linear Regression for continuous variable\n + Multiple continuous Variable\n + Interaction between variables\n + Categorical variable\n + Binomial model\n + Beta binomial\n + Poisson model\n + Gamma-Poisson\n + Multinomial\n + Dirichlet model\n + Zero inflated\n + Varying intercept\n + Varying slopes\n + Gaussian processes\n + Measuring error\n + Latent variable]\n + PCA\n + GMM\n + DPMM\n + Network model\n + Network with block model\n + Network control for data collection biases \n + BNN\n \n+ Model diagnostics (using ARVIZ):\n + Data frame with summary statistics\n + Plot posterior densities\n + Bar plot of the autocorrelation function (ACF) for a sequence of data\n + Plot rank order statistics of chains\n + Forest plot to compare HDI intervals from a number of distributions\n + Compute the widely applicable information criterion\n + Compare models based on their expected log pointwise predictive density (ELPD)\n + Compute estimate of rank normalized split-R-hat for a set of traces\n + Calculate estimate of the effective sample size (ESS)\n + Pair plot\n + Density plot\n + ESS evolution plot\n \n\n\n# Why?\n## 1. To learn\n\n## 2. Easy Model Building:\nThe following linear regression model (rethinking 4.Geocentric Models): \n$$\n\\text{height} \\sim \\mathrm{Normal}(\\mu,\\sigma)\n$$\n\n$$\n\\mu = \\alpha + \\beta \\cdot \\text{weight}\n$$\n\n$$\n\\alpha \\sim \\mathrm{Normal}(178,20)\n$$\n\n$$\n\\beta \\sim \\mathrm{Normal}(0,10)\n$$\n\n$$\n\\sigma \\sim \\mathrm{Uniform}(0,50)\n$$\n \ncan be declared in the package as\n```\nfrom BI import bi\n\n# Setup device------------------------------------------------\nm = bi(platform='cpu')\n\n# Import Data & Data Manipulation ------------------------------------------------\n# Import\nfrom importlib.resources import files\ndata_path = files('BI.resources.data') / 'Howell1.csv'\nm.data(data_path, sep=';') \nm.df = m.df[m.df.age > 18] # Manipulate\nm.scale(['weight']) # Scale\n\n# Define model ------------------------------------------------\ndef model(weight, height): \n a = m.dist.normal(178, 20, name = 'a') \n b = m.dist.lognormal(0, 1, name = 'b') \n s = m.dist.uniform(0, 50, name = 's') \n m.normal(a + b * weight , s, obs = height) \n\n# Run mcmc ------------------------------------------------\nm.fit(model) # Optimize model parameters through MCMC sampling\n\n# Summary ------------------------------------------------\nm.summary() # Get posterior distributions\n``` \n\n# Todo \n1. GUI \n2. Documentation\n3. Implementation of additional MCMC sampling methods\n\n\n",
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