Density evolution for LDPC codes construction under AWGN-channel: reciprocal-channel approximation (RCA), Gaussian Evolution, Covariance Evolution
Distributed Random Forest in Apache Flink
Quantum Position-Basis Lab (QPL) is a physics-focused Python lab that turns the position-basis identities from Leonard Susskind’s Advanced Quantum Mechanics (Lecture 7) into runnable, grid-based numerical experiments. It demonstrates the Dirac delta’s sifting property via Gaussian approximations, enforces wavefunction normalization, and visualizes
Approximations for the Wright-Fisher model of population dynamics under natural selection at two linked loci
Code to implement the approximate Gibbs sampler for efficient inference of the hierarchical Bayesian model for grouped count data. The performance of the proposed sampler is compared against that of the start-of-the-art algorithm, the No-U-Turn-Sampler (NUTS) used by default in Stan.
Add a description, image, and links to the gaussian-approximation topic page so that developers can more easily learn about it.
To associate your repository with the gaussian-approximation topic, visit your repo's landing page and select "manage topics."