ramikrispin, to python
@ramikrispin@mstdn.social avatar

(1/2) A new release to PyMC 🚀🚀🚀

This week, PyMC version v5.13.0 was released. PyMC is one of the main 🐍 libraries for 𝐁𝐚𝐲𝐞𝐬𝐢𝐚𝐧 statistics ❤️. It provides a framework for probabilistic programming, enabling users to build models with a simple Python API and fit them using 𝐌𝐚𝐫𝐤𝐨𝐯 𝐂𝐡𝐚𝐢𝐧 𝐌𝐨𝐧𝐭𝐞 𝐂𝐚𝐫𝐥𝐨 (MCMC) methods 🚀.

The new release includes new features, bug fixes 🐞, and documentation improvements 📖. More details on the release notes 📝 👇

mzloteanu, to random
@mzloteanu@mastodon.social avatar

#statstab #64 Using tidybayes with the posterior package by @mjskay

Thoughts: Going #bayesian means a lot of wrangling the posteriors to get the info you want. A crucial skills I'm still learning, but invaluable.

#bayes #rstats #tidybayes #brms


bentoh, to statistics
@bentoh@mastodon.online avatar

The MCMC sampling is simultaneously finished and unfinished before you wake your computer monitor and look at the progress bar. It's the Schrodinger's MCMC.

#Statistics #Bayesian #Rstats

jonny, (edited ) to python
@jonny@neuromatch.social avatar

I'm looking for reviewers for two packages at the moment:

Automata (@pyOpenSci )
Review: https://github.com/pyOpenSci/software-submission/issues/152
Repo: https://github.com/caleb531/automata
A #Python library for simulating finite #automata, pushdown automata, and Turing machines.

( @joss )
Review: https://github.com/openjournals/joss-reviews/issues/6193
Repo: https://sr.ht/~lsandig/Kirstine.jl
A #Julia package for Bayesian optimal experimental design with nonlinear regression models.

You'll be working with another reviewer to read and run the code, make sure it fills a basic checklist which usually only takes a few hours, and beyond that whatever youd like to focus on. Both of these are collaborative review processes where the goal is to help these packages be usable, well documented, and maintainable for the overall health of free scientific software.

Its fun, I promise! Happy to answer questions and boosts welcome.

Edit: feel free to volunteer as a reply here, DM me, or commenting on those issues! Anyone is welcome! Some experience with the language required, but other than that I can coach you through the rest.

#PeerReview #OpenReview #CodeReview #FiniteAutomata #TuringMachines #Bayesian #Regression #Statistics

ramikrispin, to datascience
@ramikrispin@mstdn.social avatar

(1/2) Intro to Bayesian Statistics with R 🚀

Following my previous posts on Bayesian Statistics, if you are looking for a resource to get started with, I recommend watching this great workshop by Angelika Stefan at R-Ladies Amsterdam meetup 👇🏼


The workshop focuses on the foundation of Bayesian statistics and covers topics such as:
✅ Parameter estimation
✅ Prior and posterior distribution
✅ Likelihood

Image credit: slides
#rstats #DataScience #bayesian #MachineLearning

@ramikrispin@mstdn.social avatar


While the workshop is with R, it is straightforward to follow along and apply with any other language, such as Python or Julia.

Slides and code: https://github.com/rladiesamsterdam/2021_Sept_Bayesian_statistics

#rstats #stats #bayesian #MachineLearning #DataScience

ramikrispin, to python
@ramikrispin@mstdn.social avatar

(1/3)Modeling Short Time Series with Prior Knowledge in PyMC 🚀

Yesterday, I shared an article by Tim Radtke about forecasting insufficient time series data with a Bayesian approach using R. Here is the Python version 🧵👇🏼

@ramikrispin@mstdn.social avatar

(2/3) The TLDR is when you need to model a short time series (less than one seasonal cycle) and have some knowledge or assumption about the expected behavior of the series - either from a similar series (i.e., similar products or geos) you can translate those assumptions to the model's prior distributions and use it to build a forecasting model.

@ramikrispin@mstdn.social avatar

(3/3) Here is the Python version of this approach by Juan Camilo Orduz using Python 🐍 with PyMC:

ramikrispin, to datascience
@ramikrispin@mstdn.social avatar

(1/2) Modeling Short Time Series with Prior Knowledge

When modeling time series data, you may find yourself with insufficient data. Insufficient data in time series would typically be defined as less than one seasonal cycle. This would challenge us to understand whether some events are driven by seasonality or other reasons, such as one-time events, outliers, etc.

@ramikrispin@mstdn.social avatar

(2/2) If you have some prior knowledge about the series (e.g., learning from similar products or goes, etc.), you should consider using the Bayesian approach.

The article below by Tim Radtke provides an example of how to incorporate prior assumptions into a time series forecasting model when having insufficient data.


ma_delsuc, to random
@ma_delsuc@fediscience.org avatar
LearnBayesStats, to paradox

📢 Episode 97 with Allen Downey is out!
In this episode, we cover

Tune in here:

UlrikeHahn, to cogsci
@UlrikeHahn@fediscience.org avatar

We have a new pape on polarisation with an #ABM of naïve Bayesian agents. It ends a decade of thinking about #testimony from a #Bayesian perspective, so I thought I’d summarise that decade in a thread.

The Issue: Much of what we believe to ‘know’ we know through the testimony of others. Intuitively, how much I adjust my beliefs in response to you saying “it is snowing” should depend on how reliable/accurate you are (ie the likelihoods associated with your report) 1/9


ramikrispin, to python
@ramikrispin@mstdn.social avatar

Getting started with Statistical Rethinking 2024 🤩

ramikrispin, to python
@ramikrispin@mstdn.social avatar

(1/6)This time of the year ☃️...Statistical Rethinking 2024 ❤️❤️❤️

This has become a tradition. Like previous Decembers, this week, the 2024 edition of the Statistical Rethinking course was announced. If you are looking to learn Bayesian statistics, I highly recommend checking it out.


Image credit: Last year course
#rstats #Stats #python #julialang #bayesian #DataScience

jobRxiv, to random
@jobRxiv@mas.to avatar
lakens, to random
@lakens@mastodon.social avatar

People are often surprised to learn they can design a study where you look at the data twice (a sequential design) at no cost (because the alpha correction is so small, there is no increase in the sample size). To learn how, see https://lakens.github.io/statistical_inferences/10-sequential.html#sample-size-for-sequential-designs

@mzloteanu@mastodon.social avatar

@lakens wait until they find out they can look at the data how many times they want if they go #Bayesian 😉

mattodon, (edited ) to haskell
@mattodon@fosstodon.org avatar

Nice work by @turion integrating live Bayesian learning into a Functional Reactive Programming app. That's the power of embedded probabilistic programming languages like Monad-Bayes:



avehtari, to random
@avehtari@bayes.club avatar

Bayesian cross-validation by parallel Markov Chain Monte Carlo by Alex Cooper, Aki Vehtari, Catherine Forbes, Lauren Kennedy, and Dan Simpson. http://arxiv.org/abs/2310.07002

  • fast general parallel brute force Bayesian cross-validation with GPUs
  • constant memory streaming estimates and convergence diagnostics
  • assessing convergence (Rhat) and accuracy (MCSE) of aggregated result from parallel computations

Alex did awesome job wih this!

#Bayesian #mcmc #parallel

avehtari, to random
@avehtari@bayes.club avatar

In my Bayesian Data Analysis course this week I explained the Metropolis algorithm and next week I'll explain HMC and NUTS. For demonstrating these, Chi Feng's MCMC interactive demos https://chi-feng.github.io/mcmc-demo/ have been super useful!

#mcmc #bayesian

pixeltracker, to random
@pixeltracker@sigmoid.social avatar

New book by Wei Ji Ma, @kordinglab and Daniel Goldreich: “ Models of and Action – An Introduction”.

“An accessible introduction to constructing and interpreting Bayesian models of perceptual and action.”

🌏 https://mitpress.mit.edu/9780262372824/bayesian-models-of-perception-and-action/

avehtari, to random
@avehtari@bayes.club avatar

New paper Past, Present, and Future of Software for Bayesian Inference by Erik Štrumbelj, Alexandre Bouchard-Côté, Jukka Corander, Andrew Gelman, Håvard Rue, Lawrence Murray, Henri Pesonen, Martyn Plummer, and I.

This review aims to summarize the most popular software for Bayesian inferenve and provide a useful map for a reader to navigate the world of Bayesian computation.

Accepted to Statistical Science with preprint available at http://www.stat.columbia.edu/~gelman/research/published/Bayesian_software_review-8.pdf

#Bayes #Bayesian

andrew, to random
@andrew@fediscience.org avatar

Unreasonably pleased with how this final image turned out #rstats #ggplot #tidybayes #bayesian

Ordered Beta regression models rule and everyone should try them out https://www.robertkubinec.com/post/limited_dvs/

fbpsy, to statistics Spanish
@fbpsy@paquita.masto.host avatar

Data aggregation can lead to biased inferences in Bayesian linear mixed models and Bayesian ANOVA: A simulation study
#statistics #bayesian

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