pglpm<p>Dear R community, I'd like to poll your opinions and ideas about the arguments of a possible R function:</p><p>Suppose you're working with the variates of some population; for instance the variates `species`, `island`, `bill_len`, `bill_dep`, `body_mass`, etc. of the `penguins` dataset <<a href="https://cran.r-project.org/package=basepenguins" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">cran.r-project.org/package=bas</span><span class="invisible">epenguins</span></a>>.</p><p>Suppose there's a package that allows you to calculate conditional probabilities of single or joint variates; for example</p><p>Pr( bill_len > 40, species = 'Adelie' | bill_dep < 16, body_mass = 4200)</p><p>and note in particular that this probability refers to intervals/tails ("bill_len > 40") as well as to point-values ("body_mass = 4200").</p><p>In fact the crucial point here is that with this function you can inquiry about the probability of a point value, "=", or about a cumulative probability, ">" or "<", or mixtures thereof, as you please.</p><p>Now what would be the "best" way to input this kind of choice as an argument to the function? Let's say you have the following two input ways:</p><p>**A: indicate the request of a cumulative probability in the variate name:**</p><p>```<br>Pr(<br> Y = list('bill_len>' = 40, species = 'Adelie'), <br> X = list('bill_dep<' = 16, body_mass = 4200)<br>)<br>```</p><p>**B: indicate the request of a cumulative probability in a separate function argument:**</p><p>```<br>Pr(<br> Y = list(bill_len = 40, species = 'Adelie'), <br> X = list(bill_dep = 16, body_mass = 4200),<br> tails = list(bill_len = '>', bill_dep = '<') # or +1, -1 instead of '>', '<'?<br>)<br>```</p><p>Any other ideas? Feel free to comment :) See <<a href="https://pglpm.github.io/inferno/reference/Pr.html" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">pglpm.github.io/inferno/refere</span><span class="invisible">nce/Pr.html</span></a>> for a clearer idea about such a function.</p><p>Thank you so much for your help!</p><p><a href="https://c.im/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstats</span></a> <a href="https://c.im/tags/bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bayesian</span></a></p>
#bayesian
2 posts2 participants0 posts today
New on the blog: Using Bayesian tools to be a better frequentist
Turns out that for negative binomial regression with small samples, standard frequentist tools fail to achieve their stated goals. Bayesian computation ends up providing better frequentist guarantees. Not sure this is a general phenomenon, just a specific example.
https://www.martinmodrak.cz/2025/07/09/using-bayesian-tools-to-be-a-better-frequentist/
#statstab #382 The JASP Guidelines for Conducting and Reporting a Bayesian Analysis
Thoughts: @JASPStats is often people's first attempt at Bayesian statistics. But proper inference and reporting is crucial.
#JASP #Bayesian #BayesFactor #guide #tutorial
https://link.springer.com/article/10.3758/s13423-020-01798-5
SpringerLinkThe JASP guidelines for conducting and reporting a Bayesian analysis - Psychonomic Bulletin & ReviewDespite the increasing popularity of Bayesian inference in empirical research, few practical guidelines provide detailed recommendations for how to apply Bayesian procedures and interpret the results. Here we offer specific guidelines for four different stages of Bayesian statistical reasoning in a research setting: planning the analysis, executing the analysis, interpreting the results, and reporting the results. The guidelines for each stage are illustrated with a running example. Although the guidelines are geared towards analyses performed with the open-source statistical software JASP, most guidelines extend to Bayesian inference in general.
Happy birthday Edwin T. Jaynes, champion of sound physics & probability theory! 
I find his text still the most illuminating about probability theory: <https://doi.org/10.1017/CBO9780511790423>
And also still a classic in statistical mechanics: <https://doi.org/10.1103/RevModPhys.34.143>
Dear colleagues working with Markov-chain Monte Carlo, especially for Bayesian posterior analysis:
Do you know of any Monte Carlo Standard Error (MCSE) estimators (or alternatively a p% MC error interval) for the *mean*, in the case where the posterior distribution may *not* have a finite variance (but it's known to have a finite mean)?
The paper by Vehtari & al. <https://doi.org/10.1214/20-BA1221> offers "efficiency" estimates for the mean in the case of non-finite variance, as well as a method to find MC error intervals for quantiles. But I can't find there a method for MC standard error or error intervals for the *mean* in the case of non-finite variance.
Cheers!
#bayesian #bayes #mcmc @avehtari@bayes.club @avehtari@mastodon.social
Project EuclidRank-Normalization, Folding, and Localization: An Improved Rˆ for Assessing Convergence of MCMC (with Discussion)Markov chain Monte Carlo is a key computational tool in Bayesian statistics, but it can be challenging to monitor the convergence of an iterative stochastic algorithm. In this paper we show that the convergence diagnostic Rˆ of Gelman and Rubin (1992) has serious flaws. Traditional Rˆ will fail to correctly diagnose convergence failures when the chain has a heavy tail or when the variance varies across the chains. In this paper we propose an alternative rank-based diagnostic that fixes these problems. We also introduce a collection of quantile-based local efficiency measures, along with a practical approach for computing Monte Carlo error estimates for quantiles. We suggest that common trace plots should be replaced with rank plots from multiple chains. Finally, we give recommendations for how these methods should be used in practice.
Dear colleagues working with Markov-chain Monte Carlo: could you share any works that explore Markov-chain "convergence", and precision of mean estimates, with methods that use *quantiles* (or interquartile range, or median absolute deviation, or similar), rather than standard deviation and similar quantities?
Just to be clear, I don't mean estimation of quantiles, but estimation *by means of* quantiles.
Thank you!
#statstab #377 To adjust, or not to adjust, for multiple comparisons
Thoughts: Not all-encompassing, but it does cover some relevant notions about multiplicity adjustments.
#FDR #FWER #typeI #error #bonferroni #bayesian #multiplecomparisons
https://www.jclinepi.com/article/S0895-4356%2825%2900021-6/fulltext
A monument to the triumph of vanity over safety?
Gene expression is tuned by #epigenetic changes, which explains why, say, liver and skin cells have the same genome but are otherwise different. Here we introduce a
#Bayesian model for the analysis of epigenetic changes during development. https://epigeneticsandchromatin.biomedcentral.com/articles/10.1186/s13072-025-00594-6
BioMed CentralBath: a Bayesian approach to analyze epigenetic transitions reveals a dual role of H3K27me3 in chondrogenesis - Epigenetics & ChromatinBackground Histone modifications are key epigenetic regulators of cell differentiation and have been intensively studied in many cell types and tissues. Nevertheless, we still lack a thorough understanding of how combinations of histone marks at the same genomic location, so-called chromatin states, are linked to gene expression, and how these states change in the process of differentiation. To receive insight into the epigenetic changes accompanying the differentiation along the chondrogenic lineage we analyzed two publicly available datasets representing (1) the early differentiation stages from embryonic stem cells into chondrogenic cells and (2) the direct differentiation of mature chondrocyte subtypes. Results We used ChromHMM to define chromatin states of 6 activating and repressive histone marks for each dataset and tracked the transitions between states that are associated with the progression of differentiation. As differentiation-associated state transitions are likely limited to a reduced set of genes, one challenge of such global analyses is the identification of these rare transitions within the large-scale data. To overcome this problem, we have developed a relativistic approach that quantitatively relates transitions of chromatin states on defined groups of tissue-specific genes to the background. In the early lineage, we found an increased transition rate into activating chromatin states on mesenchymal and chondrogenic genes while mature chondrocytes are mainly enriched in transition between activating states. Interestingly, we also detected a complex extension of the classical bivalent state (H3K4me3/H3K27me3) consisting of several activating promoter marks besides the repressive mark H3K27me3. Within the early lineage, mesenchymal and chondrogenic genes undergo transitions from this state into active promoter states, indicating that the initiation of gene expression utilizes this complex combination of activating and repressive marks. In contrast, at mature differentiation stages the inverse transition, the gain of H3K27me3 on active promoters, seems to be a critical parameter linked to the initiation of gene repression in the course of differentiation. Conclusions Our results emphasize the importance of a relative analysis of complex epigenetic data to identify chromatin state transitions associated with cell lineage progression. They further underline the importance of serial analysis of such transitions to uncover the diverse regulatory potential of distinct histone modifications like H3K27me3.
Self promotion
Die Luxusjacht "Bayesian" ist aus dem Meer geborgen! 
Nach langer Zeit wurde das spektakuläre Wrack erfolgreich geborgen. Mehr dazu im Artikel: https://www.n-tv.de/panorama/Luxusjacht-Bayesian-endlich-aus-dem-Meer-geborgen-article25849786.html #Luxusjacht #Bayesian #Bergung #News
#newz
n-tv NACHRICHTEN · Als unsinkbar deklariert: Luxusjacht "Bayesian" endlich aus dem Meer geborgen
Sunken British superyacht Bayesian is raised from the seabed.
A superyacht that sank off the coast of the Italian island of Sicily last year has been raised from the seabed by a specialist salvage team.
Seven of the 22 people on board died in the sinking, including the vessel's owner, British tech tycoon Mike Lynch and his 18-year-old daughter.
The cause of the sinking is still under investigation.
BBC · Sunken British superyacht Bayesian is raised from the seabed.
https://www.europesays.com/de/207526/ Vor Sizilien: Luxusjacht „Bayesian“ wird geborgen #Bayesian #Headlines #Nachrichten #News #Schiffsunglück #Schlagzeilen #TopNews #TopMeldungen #TopMeldungen #TopNews
Vor Sizilien: Luxusjacht "Bayesian" wird geborgen
Die Bergung des 56 Meter langen Segelschiffes hatte sich mehrfach verzögert. Nun ist es an der Oberfläche. Bei ihrem Untergang starben der britische Milliardär Mike Lynch und sechs weitere Insassen.
tagesschau.de · Vor Sizilien: Luxusjacht "Bayesian" wird geborgenDie Bergung des 56 Meter langen Segelschiffes hatte sich mehrfach verzögert. Nun ist es an der Oberfläche. Bei ihrem Untergang starben der britische Milliardär Mike Lynch und sechs weitere Insassen.
Das Wrack wurde jetzt vor Sizilien aus 50 Metern Tiefe an die Wasseroberfläche gehoben.
#Bayesian #MikeLynch
https://floatmagazin.de/leute/luxusjacht-bayesian-von-meeresgrund-gehoben/
Interested in trying out *Bayesian nonparametrics* for your statistical research?
I'd be very grateful if people tried out this R package for Bayesian nonparametric population inference, called "inferno" :
<https://pglpm.github.io/inferno/>
It is especially addressed to clinical and medical researchers, and allows for thorough statistical studies of subpopulations or subgroups.
Installation instructions are here: <https://pglpm.github.io/inferno/index.html#installation>.
A step-by-step tutorial, guiding you through an example analysis of a simple dataset, is here: <https://pglpm.github.io/inferno/articles/vignette_start.html>.
The package has already been tested and used in concrete research about Alzheimer's Disease, Parkinson's Disease, drug discovery, and applications to machine learning.
Feedback is very welcome. If you find the package useful, feel free to advertise it a little :)
pglpm.github.ioBayesian nonparametric population inferenceFunctions for Bayesian nonparametric population (or exchangeable or density) inference. From a machine-learning perspective, it offers a model-free, uncertainty-quantified prediction algorithm.
'How good is your Laplace approximation of the Bayesian posterior? Finite-sample computable error bounds for a variety of useful divergences', by Mikołaj J. Kasprzak, Ryan Giordano, Tamara Broderick.
http://jmlr.org/papers/v26/24-0619.html
#bayesian #posterior #laplace
#Bayesian analysis simplified (#BAYAS): our paper is out! We hope that many biologists & other users will find BAYAS helpful for experimental planning & data analysis. No programming or installation required. Will hopefully lead to reduction of lab animal numbers. #3R #bayes https://doi.org/10.1093/bioinformatics/btaf276