https://pinboard.in/u:jm/public/ recent bookmarks from jm 2020-06-13T22:42:29+00:00 https://medium.com/@Bob_Wachter/interpreting-covid-19-test-results-a-bayesian-approach-df058dad2ade jma brief tutorial on Covid-19 testing, with an emphasis on a Bayesian approach. After presenting the basics, we’ll walk through four confusing Covid-19 testing scenarios, just to give you a feel for the kinds of pickles we often find ourselves in. ]]> prevalence covid-19 bayes bayesian statistics testing https://pinboard.in/ https://pinboard.in/u:jm/b:419f48573a05/ 2015-01-31T11:10:18+00:00 http://www.quickmeme.com/scumbag-data-scientist jm funny data-science statistics machine-learning hadoop bayes memes image-macros https://pinboard.in/ https://pinboard.in/u:jm/b:c2158da0bb75/ 2014-09-15T10:50:48+00:00 http://google-opensource.blogspot.ie/2014/09/causalimpact-new-open-source-package.html jmHow can we measure the number of additional clicks or sales that an AdWords campaign generated? How can we estimate the impact of a new feature on app downloads? How do we compare the effectiveness of publicity across countries? In principle, all of these questions can be answered through causal inference. In practice, estimating a causal effect accurately is hard, especially when a randomised experiment is not available. One approach we've been developing at Google is based on Bayesian structural time-series models. We use these models to construct a synthetic control — what would have happened to our outcome metric in the absence of the intervention. This approach makes it possible to estimate the causal effect that can be attributed to the intervention, as well as its evolution over time. We've been testing and applying structural time-series models for some time at Google. For example, we've used them to better understand the effectiveness of advertising campaigns and work out their return on investment. We've also applied the models to settings where a randomised experiment was available, to check how similar our effect estimates would have been without an experimental control. Today, we're excited to announce the release of CausalImpact, an open-source R package that makes causal analyses simple and fast. With its release, all of our advertisers and users will be able to use the same powerful methods for estimating causal effects that we've been using ourselves. Our main motivation behind creating the package has been to find a better way of measuring the impact of ad campaigns on outcomes. However, the CausalImpact package could be used for many other applications involving causal inference. Examples include problems found in economics, epidemiology, or the political and social sciences. ]]> causal-inference r google time-series models bayes adwords advertising statistics estimation metrics https://pinboard.in/ https://pinboard.in/u:jm/b:a62b2f300071/ 2014-03-12T15:33:10+00:00 http://www.bea.aero/fr/enquetes/vol.af.447/metron.search.analysis.pdf jm metron bayes bayesian-inference machine-learning statistics via:jgc air-france disasters probability inference searching https://pinboard.in/ https://pinboard.in/u:jm/b:e7c127ca54da/ 2011-10-03T10:48:12+00:00 http://www.guardian.co.uk/law/2011/oct/02/formula-justice-bayes-theorem-miscarriage jm uk law guardian via:fanf bayes maths statistics legal https://pinboard.in/ https://pinboard.in/u:jm/b:0f4736885ab1/