DictDensityFit: Fit Distribution from Kernels

In lockEF/BayesianScreening: Perform Bayesian Screening with Gene Dependence

Description

Given a vector of measurements for N samples (e.g, for a given methylation sites), and a collection of Gaussian kernels truncated between 0 and 1, estimate a weighted combination of kernels to approximate the distribution of measurements.

Usage

DictDensityFit(X,mu,Sigma,Concentration = 0.1,NumDraws = 1000)

Arguments

X	a vector of measurements for N samples (e.g, for a given methylation sites). The entries must be continuous between 0 and 1.
mu	Vector of kernel means.
Sigma	Vector of kernel standard deviations
Concentration	Dirichlet hyperparameter for kernel weights
NumDraws	Number of MCMC draws for posterior inference.

Value

Returns a vector giving the weight of each kernel, averaged over the MCMC draws.

Author(s)

Eric F. Lock

References

Lock, E. F. & Dunson, D. B. (2015). Shared kernel Bayesian screening. Biometrika, 102 (4): 829-842.

lockEF/BayesianScreening documentation built on May 24, 2020, 11:50 p.m.