/explore

Click through on any links that interest you or select the planets on the right to continue exploring the Outer Web.
You are here

 jmlr.org
Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting
| | weisser-zwerg.dev
Series: Monte Carlo Methods
2.5 parsecs away

Travel
| | A series about Monte Carlo methods and generating samples from probability distributions.
| | willieneis.github.io
Embarrassingly Parallel MCMC
1.1 parsecs away

Travel
| |
| | qbnets.wordpress.com
My Identifiability Checker Works! Verified Judea Pearl's Backdoor and Frontdoor adjustment formulae | Quantum Bayesian Networks
3.2 parsecs away

Travel
| | My software is working! I am ecstatic. In a previous blog post entitled "Simple, Monte Carlo driven, Pearl-identifiability checker" which I wrote 2 days ago, I described my future plans to add to my software JudeasRx, an "identifiability checker" based on a very efficient and mature MCMC (Markov Chain Monte Carlo) Python software library called...
| | peterbloem.nl
The Singular Value Decomposition | peterbloem.nl
23.9 parsecs away

Travel
| [AI summary] The pseudo-inverse is a powerful tool for solving matrix equations, especially when the inverse does not exist. It provides exact solutions when they exist and least squares solutions otherwise. If multiple solutions exist, it selects the one with the smallest norm. The pseudo-inverse can be computed using the singular value decomposition (SVD), which is numerically stable and handles cases where the matrix does not have full column rank. The SVD approach involves computing the SVD of the matrix, inverting the non-zero singular values, and then reconstructing the pseudo-inverse using the modified SVD components. This method is preferred due to its stability and ability to handle noisy data effectively.