GIMSAN: motif-finder with biologically realistic and reliable statistical significance analysis
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Updated
Jan 11, 2016 - C
GIMSAN: motif-finder with biologically realistic and reliable statistical significance analysis
R implementation of the Dirichlet Process Gaussian Mixture Model (with MCMC)
Implementation of a Gibbs-Metropolis sampling algorithm in CUDA
Gibbs sampler in C, Python, Node.js, Julia, and R
Approximate Bayesian inference for mixed effects models with heterogeneity
Sample points on a disk with radius r that no two points is closer to each other than d
Statistical Recognition Methods home tasks
Using a bayesian hierarchical model to predict the school effective index
Bayesian trend filtering micro library. http://trendpy.readthedocs.io/en/latest/
The inspections on some important literatures, mainly including codes.
A Latent Dirichlet Allocation implementation in Python.
Libary for SGPD (Sigmoidal Gaussian Process Density) inference
Generating samples from Ising model.
Estimate Barton & Lord's (1981) <doi:10.1002/j.2333-8504.1981.tb01255.x> four parameter IRT model with lower and upper asymptotes using Bayesian formulation described by Culpepper (2016) <doi:10.1007/s11336-015-9477-6>.
statistical modelling of the wine data-set available at https://www.kaggle.com/zynicide/wine-reviews
A fun package for exploring conjugate models and gibbs samplers.
Visualization of Gibbs sampling for 2D Gaussian distribution
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