PyMC (formerly PyMC3) is a Python package for Bayesian statistical modeling focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI) algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.
Check out the PyMC overview, or one of the many examples! For questions on PyMC, head on over to our PyMC Discourse forum.
- Intuitive model specification syntax, for example,
x ~ N(0,1)translates tox = Normal('x',0,1) - Powerful sampling algorithms, such as the No U-Turn Sampler, allow complex models with thousands of parameters with little specialized knowledge of fitting algorithms.
- Variational inference: ADVI for fast approximate posterior estimation as well as mini-batch ADVI for large data sets.
- Relies on PyTensor which provides:
- Computation optimization and dynamic C or JAX compilation
- NumPy broadcasting and advanced indexing
- Linear algebra operators
- Simple extensibility
- Transparent support for missing value imputation
Plant growth can be influenced by multiple factors, and understanding these relationships is crucial for optimizing agricultural practices.
Imagine we conduct an experiment to predict the growth of a plant based on different environmental variables.
import pymc as pm
# Taking draws from a normal distribution
seed = 42
x_dist = pm.Normal.dist(shape=(100, 3))
x_data = pm.draw(x_dist, random_seed=seed)
# Independent Variables:
# Sunlight Hours: Number of hours the plant is exposed to sunlight daily.
# Water Amount: Daily water amount given to the plant (in milliliters).
# Soil Nitrogen Content: Percentage of nitrogen content in the soil.
# Dependent Variable:
# Plant Growth (y): Measured as the increase in plant height (in centimeters) over a certain period.
# Define coordinate values for all dimensions of the data
coords={
"trial": range(100),
"features": ["sunlight hours", "water amount", "soil nitrogen"],
}
# Define generative model