A new Bayesian approach to incorporating the effects of dormancy (more than seasonal) into population dynamics models:
https://www.pnas.org/doi/abs/10.1073/pnas.2501394122
It would be interesting to see if this can be applied to anadromous fish that spend variable numbers of years at sea, or to species with other types of refugia.
Detective work with #genomes: some invasive species have been introduced deliberately, others inadvertently. A new #statistical framework helps to track what probably happened. Demonstration object is the Pacific oyster. #bayes https://www.pnas.org/doi/abs/10.1073/pnas.2418730122?af=R
eDNAjoint: An R package for interpreting paired or semi-paired environmental DNA and traditional survey data in a Bayesian framework
https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.70000
#statstab #295 The Fallacy of the Null-Hypothesis Significance Test
Thoughts: "the [..] aim of a scientific experiment is not to precipitate decisions, but to make an appropriate adjustment in the degree to which one accepts, or believes, the hypothesis"
#NHST #Bayes #ConfidenceIntervals #pvalues #significance #testing #hypotheses #likelihood #critique #fallacy
#statstab #293 The Bayesian Bootstrap
Thoughts: I need to think more on where bootstrapping makes sense in a bayesian setting. But here's a tutorial.
#stats #bayesian #bayes #bootstrap #resampling
https://towardsdatascience.com/the-bayesian-bootstrap-6ca4a1d45148/
#stats help: is there any online tutorial for ordinal CFA with {blavaan}?
#statstab #272 Different meanings of p-values
Thoughts: A riveting (& confusing) discussion on the definitions & properties of p-values. W/ guest appearance from some big names in stats, from all camps.
@bthalpin
Curiosity: what does it do if you ask for an 89% *credibility* interval, maybe even asking not to make distributional assumptions?
I got an email from the author promoting this benchmark comparison of #Julialang + StanBlocks + #Enzyme vs #Stan runtimes.
StanBlocks is a macro package for Julia that mimics the structure of a Stan program. This is the first I've heard about it.
A considerable number of these models are faster in Julia than Stan, maybe even most of them.
#statstab #232 Bayesian Interval-Null Testing
Thoughts: @JASPStats has a module for Equivalence Tests that include Bayesian Overlapping and Non-Overlapping Hypothesis Testing.
#equivalencetests #bayesfactors #jasp #noeffect #bayes
https://jasp-stats.org/2020/06/02/frequentist-and-bayesian-equivalence-testing-in-jasp/
„calling something logic doesn’t make it so. Calling someone rational doesn’t make it so“
I’ve been thinking for a while that, as someone who works on human rationality and rational argument, I should write a blog post on what that actually means (and, maybe more importantly, doesn‘t mean).
in the meantime, though, I found much to agree with in this piece:
Title: The magical thinking of guys who love logic
https://theoutline.com/post/7083/the-magical-thinking-of-guys-who-love-logic
#statstab #229 Prior Modeling
by @betanalpha
Thoughts: Thorough overview of the prior elicitation process and ways to think about priors.
#stats #priors #probability #metascience #bayes #bayesian
https://betanalpha.github.io/assets/case_studies/prior_modeling.html
#statstab #228 Applied Modelling in Drug Development - Setting priors in {brms}
Thoughts: Part of a larger book, useful bit for understanding how to set priors & check them for bayesian models & meta-analyses
#stats #brms #priors #metaanalysis #bayesian #r #bayes #drugs #clinicaltrials
#statstab #227 Parameterization of Response Distributions in {brms}
Thoughts: If you use #brms and can read mathematical notation (who can't, right?), this page will be useful.
#r #bayes #models #distributions #likelihood #stats
https://cran.r-project.org/web/packages/brms/vignettes/brms_families.html
Immune escape of Hepatitis B virus (HBV): based on >500 HBV genomes and clinical data we could discover many mutations by which the virus can escape immune recognition. We also show how the adaptation of HBV to the immune system changes over the course of the infection. The key tool in this collaboration of virologists and bioinformaticians was our #Bayesian HAMdetector method.
Spent the day learning to code up a non centered parameterization of a correlated varying intercepts/slopes models in #Stan! I simulated data and tested that it can recover parameters (inc. the correlations!) Now to vectorize it and generalize it for an arbitrarily long vector of predictors…
#statstab #217 The distribution of p-values obtained in replications depends only on the original p-value. How can it be true?
Thoughts: A great discussion where the author @thenewstats chimes in to explain the issue.
