<?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 on Adventures in Why</title><link>https://www.adventuresinwhy.com/tag/bayesian-statistics/</link><description>Recent content in Bayesian Statistics on Adventures in Why</description><generator>Hugo</generator><language>en-us</language><managingEditor>bob@ConvexAnalytics.com (Bob Wilson)</managingEditor><webMaster>bob@ConvexAnalytics.com (Bob Wilson)</webMaster><copyright>&amp;copy; Bob Wilson {year}</copyright><lastBuildDate>Sat, 13 Jan 2024 00:00:00 +0000</lastBuildDate><atom:link href="https://www.adventuresinwhy.com/tag/bayesian-statistics/index.xml" rel="self" type="application/rss+xml"/><item><title>Modes of Inference in Randomized Experiments</title><link>https://www.adventuresinwhy.com/post/modes_of_inference/</link><pubDate>Sat, 13 Jan 2024 00:00:00 +0000</pubDate><author>bob@ConvexAnalytics.com (Bob Wilson)</author><guid>https://www.adventuresinwhy.com/post/modes_of_inference/</guid><description>Randomization provides the &amp;ldquo;reasoned basis for inference&amp;rdquo; in an experiment. Yet some approaches to analyzing experiments ignore the special structure of randomization. Simple, familiar approaches like regression models sometimes give wrong answers when applied to experiments. Approaches exploiting randomization deliver more reliable inferences than methods neglecting it. Randomization inference should be the first method we reach for when analyzing experiments.</description></item><item><title>The Winner's Curse: Why it Happens and What to Do About It</title><link>https://www.adventuresinwhy.com/post/point_estimates/</link><pubDate>Tue, 04 Jan 2022 00:00:00 +0000</pubDate><author>bob@ConvexAnalytics.com (Bob Wilson)</author><guid>https://www.adventuresinwhy.com/post/point_estimates/</guid><description>When running an A/B test with many variants, say, more than 5, we often run into a phenomenon known as the Winner&amp;rsquo;s Curse, where the winning variant performs worse when we adopt it universally than it had during the test itself. In this post, we discuss why this phenomenon occurs and what to do about it.</description></item><item><title>Robust Portfolio Optimization in Models with Diminishing Returns</title><link>https://www.adventuresinwhy.com/post/robust_portfolio_optimization_diminishing_returns/</link><pubDate>Tue, 28 Dec 2021 00:00:00 +0000</pubDate><author>bob@ConvexAnalytics.com (Bob Wilson)</author><guid>https://www.adventuresinwhy.com/post/robust_portfolio_optimization_diminishing_returns/</guid><description>In our last post, we discussed how model uncertainty poses a risk when allocating resources among productive assets. In this post, we expand the discussion to models with diminishing returns. Such models are common in economics. As before, we can incorporate model uncertainty directly into the problem, achieving good performance regardless of the true model, with minimal impact to nominal performance. Robust optimization is both powerful and practical.</description></item><item><title>Bayesian A/B Testing Considered Harmful</title><link>https://www.adventuresinwhy.com/post/bayesian_ab_testing/</link><pubDate>Sun, 26 Sep 2021 00:00:00 +0000</pubDate><author>bob@ConvexAnalytics.com (Bob Wilson)</author><guid>https://www.adventuresinwhy.com/post/bayesian_ab_testing/</guid><description>In science we study physically meaningful quantities that have some kind of objective reality, and that means that multiple people should draw substantively equivalent conclusions. But in some situations, this principle is at odds with the Bayesian Coherency Principle, and so we have to choose between internal consistency, or consistency with external reality.</description></item></channel></rss>