Explore >> Select a destination
|
You are here 
|
andrewjaffe.net | ||
| | | | |
alexanderetz.com
|
|
| | | | | [This post has been updated and turned into a paper to be published in AMPPS] Much of the discussion in psychology surrounding Bayesian inference focuses on priors. Should we embrace priors, or should we be skeptical? When are Bayesian methods sensitive to specification of the prior, and when do the data effectively overwhelm it? Should... | |
| | | | |
deepai.org
|
|
| | | | | Bayesian inference refers to the application of Bayes' Theorem in determining the updated probability of a hypothesis given new information. | |
| | | | |
easystats.github.io
|
|
| | | | | TLDR: BayestestR currently uses a 89% threshold by default for Credible Intervals (CI). Should we change that? If so, by what? Join the discussion here. Magical numbers, or conventional thresholds, have bad press in statistics, and there are many of them. For instance, .05 (for the p-value), or the 95% range for the Confidence Interval (CI). Indeed, why 95 and not 94 or 90? One of the issue that traditional confidence intervals are often being interpreted as a description of the uncertainty surrounding a parameter's value. | |
| | | | |
summerseminarphilstat.com
|
|
| | | Final Schedule Special Invited Speakers Participants FROM 2019: GOALS AND INSTRUCTIONS (3/9/19) OVERVIEW SELECTION CRITERIA APPLICATION INSTRUCTIONS (PDF) STIPEND, EXPECTATIONS, AND CONDITIONS OF AWARD CHECKLIST OF APPLICATION MATERIALS OVERVIEW We will offer a 2-week immersive seminar on Philosophy of Statistics (PhilStat) at Virginia Tech for faculty and post-docs in philosophy who wish to acquire... | ||
