<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Bayesian Statistics |</title><link>https://loganblaine.com/tags/bayesian-statistics/</link><atom:link href="https://loganblaine.com/tags/bayesian-statistics/index.xml" rel="self" type="application/rss+xml"/><description>Bayesian Statistics</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Thu, 01 Jan 2026 00:00:00 +0000</lastBuildDate><image><url>https://loganblaine.com/media/icon_hu_eee4a95885829ab2.png</url><title>Bayesian Statistics</title><link>https://loganblaine.com/tags/bayesian-statistics/</link></image><item><title>A scalable Bayesian analysis method for large single cell CRISPR screens</title><link>https://loganblaine.com/publications/blaine-2026-perturbo/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://loganblaine.com/publications/blaine-2026-perturbo/</guid><description/></item><item><title>PerTurbo</title><link>https://loganblaine.com/projects/perturbo/</link><pubDate>Sun, 01 Sep 2024 00:00:00 +0000</pubDate><guid>https://loganblaine.com/projects/perturbo/</guid><description>&lt;p&gt;PerTurbo is a scalable Bayesian analysis framework for large-scale Perturb-seq screens, with a GPU-accelerated implementation of the probabilistic model using PyTorch, Pyro, and Lightning.&lt;/p&gt;
&lt;p&gt;See the associated manuscript, &lt;em&gt;A scalable Bayesian analysis method for large single cell CRISPR screens&lt;/em&gt; (in preparation).&lt;/p&gt;</description></item></channel></rss>