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Bayesian Modeling and Probabilistic Programming in Python
PyMC is a Python library for probabilistic programming focused on Bayesian statistical modeling and machine learning. Built on top of computational tools like Aesara and NumPy, PyMC allows users to define models using intuitive syntax and perform inference using MCMC, variational inference, and other advanced algorithms. It’s widely used in scientific research, data science, and decision modeling.
Deconvolutes qPCR data to estimate cell-type-specific gene expression
DEBay: Deconvolution of Ensemble through Bayes-approach
DEBay estimates cell type-specific gene expression by deconvolution of quantitative PCR data of a mixed population. It will be useful in experiments where the segregation of different cell types in a sample is arduous, but the proportion of different cell types in the sample can be measured. DEBay uses the population distribution data and the qPCR data to calculate the relative expression of the target gene in different cell types in...
A brief summary of the main features of Fesslix:
- Perform non-intrusive reliability analysis or Bayesian updating either
--- by running commands on the command line or
--- by means of an Octave interface or
--- by means of a Python interface
- Flexible input language for writing Fesslix parameter files
--- Control flow statements (e.g. if, for, while)
--- Most parameters can be defined as functions
- Working with response surfaces
- Linear finite element analysis using truss, beam and plane stress/strain elements
- Spectral Stochastic Finite Elements
- Bayesian networks
This is the download page for Windows executables of Fesslix.