#bayesian-statistics — Public Fediverse posts

"Five skills. Each one is counter-cyclical (becomes more valuable as hype recedes), resistant to LLM automation (requires human judgment that pattern-matching can’t replicate), and directly tied to the business outcomes executives actually pay for."
by Kaushik Rajan: https://towardsdatascience.com/the-ai-bubble-has-a-data-science-escape-hatch/

#DataScience #BayesianStatistics #BayesianStats #Bayesian #causalInference #experimentalDesign #SPC #statisticalProcessControl

Bayesian statistics for confused data scientists

https://nchagnet.pages.dev/blog/bayesian-statistics-for-confused-data-scientists/

#HackerNews #BayesianStatistics #ConfusedDataScientists #DataScience #Learning #Analytics

R and Security!

From the Risk 2026 talk “A Bayesian R Framework for Quantifying Cyber Risk Using the FAIR Model and MITRE ATT&CK”:

A fully open, R-based quantitative cyber-risk model combining FAIR + MITRE ATT&CK. Uses {cmdstanr}, Bayesian inference, and Monte Carlo to estimate ALE, incident frequency, and loss exceedance curves—transparent + reproducible.

Abstract: https://rconsortium.github.io/Risk_website/Abstracts.html#joshua-connors

Register for Risk 2026!

#RStats #CyberSecurity #BayesianStatistics #RiskManagement

My Road to Bayesian Stats

By 2015, I had heard of Bayesian Stats but didn’t bother to go deeper into it. After all, significance stars, and p-values worked fine. I started to explore Bayesian Statistics when considering small sample sizes in biological experiments. How much can you say when you are comparing means of 6 or even 60 observations? This is the nature work at the edge of knowledge. Not knowing what to expect is normal. Multiple possible routes to a seen a result is normal. Not knowing how to pick the route to the observed result is also normal. Yet, our statistics fails to capture this reality and the associated uncertainties. There must be a way I thought. 

I started by searching for ways to overcome small sample sizes. There are minimum sample sizes recommended for t-tests. Thirty is an often quoted number with qualifiers. Bayesian stats does not have a minimum sample size. This had me intrigued. Surely, this can’t be a thing. But it is. Bayesian stats creates a mathematical model using your observations and then samples from that model to make comparisons. If you have any exposure to AI, you can think of this a bit like training an AI model. Of course the more data you have the better the model can be. But even with a little data we can make progress. 

How do you say, there is something happening and it’s interesting, but we are only x% sure. Frequentist stats have no way through. All I knew was to apply the t-test and if there are “***” in the plot, I’m golden. That isn’t accurate though. Low p-values indicate the strength of evidence against the null hypothesis. Let’s take a minute to unpack that. The null hypothesis is that nothing is happening. If you have a control set and do a treatment on the other set, the null hypothesis says that there is no difference. So, a low p-value says that it is unlikely that the null hypothesis is true. But that does not imply that the alternative hypothesis is true. What’s worse is that there is no way for us to say that the control and experiment have no difference. We can’t accept the null hypothesis using p-values either. 

Guess what? Bayes stats can do all those things. It can measure differences, accept and reject both  null and alternative hypotheses, even communicate how uncertain we are (more on this later). All without making assumptions about our data.

It’s often overlooked, but frequentist analysis also requires the data to have certain properties like normality and equal variance. Biological processes have complex behavior and, unless observed, assuming normality and equal variance is perilous. The danger only goes up with small sample sizes. Again, Bayes requires you to make no assumptions about your data. Whatever shape the distribution is, so called outliers and all, it all goes into the model. Small sample sets do produce weaker fits, but this is kept transparent. 

Transparency is one of the key strengths of Bayesian stats. It requires you to work a little bit harder on two fronts though. First you have to think about your data generating process (DGP). This means how do the data points you observe came to be. As we said, the process is often unknown. We have at best some guesses of how this could happen. Thankfully, we have a nice way to represent this. DAGs, directed acyclic graphs, are a fancy name for a simple diagram showing what affects what. Most of the time we are trying to discover the DAG, ie the pathway of a biological outcome. Even if you don’t do Bayesian stats, using DAGs to lay out your thoughts is a great. In Bayesian stats the DAGs can be used to test if your model fits the data we observe. If the DAG captures the data generating process the fit is good, and not if it doesn’t. 

The other hard bit is doing analysis and communicating the results. Bayesian stats forces you to be verbose about your assumptions in your model. This part is almost magicked away in t-tests. Frequentist stats also makes assumptions about the model that your data is assumed to follow. It all happens so quickly that there isn’t even a second to think about it. You put in your data, click t-test and woosh! You see stars. In Bayesian stats stating the assumptions you make in your model (using DAGs and hypothesis about DGPs) communicates to the world what and why you think this phenomenon occurs. 

Discovering causality is the whole reason for doing science. Knowing the causality allows us to intervene in the forms of treatments and drugs. But if my tools don’t allow me to be transparent and worse if they block people from correcting me, why bother?

Richard McElreath says it best:

There is no method for making causal models other than science. There is no method to science other than honest anarchy.

#AI #BayesianStatistics #BiologicalDataAnalysis #Business #CausalInference #DAGs #DataGeneratingProcess #dataScience #ExperimentalDesign #FrequentistVsBayesian #Leadership #machineLearning #philosophy #science #ScientificMethod #SmallSampleSize #StatisticalModeling #StatisticalPhilosophy #statistics #TransparentScience #UncertaintyQuantification

Any academics know of #BayesianStatistics or #datascience winter schools in Europe?
#academicchatter #WinterSchool

Question for all the #BayesianStatistics and #epistemology folks: can a Bayesian falsify things? Or is falsification just a different updating function for knowledge?
Reason for saying this: falsification to me is kind of a Schumpeterian version of epistemology. You tear down a building (theory) to get a new, better one. Bayesianism on the other hand is more akin to a formative evaluation.

Der "britische Tech Milliardär", der vermisst wird, heißt Mike Lynch. Die gesunkene Jacht gehört ihm Und hieß "Bayesian". Reich geworden ist er mit der Firma "Autonomy" die Pattern Matching mit "Bayesian Inference" betrieb. 2011 wurde Autonomy an HP verkauft und ging ein. Lynch wurde des Betrugs beschuldigt, an die USA ausgeliefert, im März 2024 vor gericht gestellt und im juni 2024 freigesprochen. Seit 2013 betreibt er die CyberSecurity firma DarkTrace.

https://en.wikipedia.org/wiki/Mike_Lynch_%28businessman%29

@HonkHase @geist @kkarhan

#datenschutz #MikeLynch #IT #Bayesian #BayesianStatistics #cybersecurity

Everything Is Predictable: How Bayesian Statistics Explain Our World by Tom Chivers, 2024

A captivating and user-friendly tour of Bayes's theorem and its global impact on modern life from the acclaimed science writer and author of The Rationalist's Guide to the Galaxy.

@bookstodon
#books
#nonfiction
#probability
#BayesianStatistics
#BayesTheorem

New on the blog: showcasing the immense hackability of #brms by extending a random intercept model with linear predictors on the standard deviation of the random intercept. Should you do it? Most likely not, but if you really really want, there is a way. Also the techniques shown are general and let you do a lot of other crazy stuff with brms. Happy for any feedback!
https://www.martinmodrak.cz/2024/02/17/brms-hacking-linear-predictors-for-random-effect-standard-deviations/

#bayesian #BayesianStatistics #BayesianInference #MixedModels

Been looking forward to this #ElixirConf talk(“#ExStan: Elixir 🤝 Stan for Probabilistic Modeling”) by young master Shubham Gupta.
Mostly because I want this package to be released to Hex so I can play with #BayesianStatistics in #ElixirLang. Also it is a big stepping stone to porting the Prophet time-series prediction library to Elixir by someone. Hopefully someone smarter than me though.

https://www.elixirconf.eu/talks/exstan-elixir-stan-for-probabilistic-modeling/

I will take Bayesians' criticisms of frequentist approaches more seriously when I finally hear a Bayesian statistician actually present a reasonable approximation to a frequentist analysis, rather than engaging in low parody.

Note that this is a criticism of people, not of any particular statistical method or theory. Also, every statistician I work with uses multiple frameworks for their analyses so 乁⁠(⁠ ⁠•⁠_⁠•⁠ ⁠)⁠ㄏ.

#statistics #stats #math #computationalscience #bayesian #BayesianStatistics

Since September, I've embarked on a permanent position at SCIMABIO-Interface. Excited to apply #PopulationDynamics #BehaviouralEcology & #BayesianStatistics to engage w. managers & stakeholders. Science-Policy Interface is essential for effective #Fish #Conservation! 🐟

https://www.scimabio-interface.fr

Is there a good reason to report 94% HDI (highest density intervals) for regression coefficients instead of 95% HDI? #Bayes #Bayesian #BayesianStatistics

My latest blog post illustrates simple Bayesian inference by using it to predict fishing success. Inspired by an excellent paper by Rafal Bogacz (2017) and a cool blog by Fabian Dablander neither of whom are on Mastodon afaik https://samplingdistribution.blogspot.com/2023/04/using-bayesian-inference-to-predict.html?m=1 #BayesianStatistics

Big shoutout to our instructor @SolomonKurz & this interactive group of attendees for a fantastic course on Bayesian statistics using brms and the tidyverse🙌📈

#BayesianStatistics #brms #Tidyverse #Rstats

#DavidKipping and #LexFridman - Do we live in a #Simulation?

https://www.youtube.com/watch?v=u201Bb0ev88&ab_channel=LexClips

#Science #Philosophy #Logic #Statistics #Probability #Bayesian #BayesianPrior #BayesianPriors #BayesianStatistics #SimulationHypothesis #TheSimulationHypothesis #AncestorSimulation #Universe #SimulatedUniverse #Bostrom #NickBostrom

#introduction post. I'm a software developer, originally with a background in math. I mostly program in #python, #bash and #javascript, though also dabble in #prolog. I'm slowly teaching myself #BayesianStatistics. I'm a maintainer of the Parsimonious Python parsing library.

* StackOverflow: https://stackoverflow.com/users/303931/lucas-wiman
* Github: https://github.com/lucaswiman
* Infrequently updated blog: https://lucaswiman.github.io

Feel free to hit me up with #regex or Python puzzles.

Hey! I'm a biologist/ecologist specialising in #biodiversitychange and #bayesianstatistics. From Catalogne and now based in Quebec, I am doing my Ph.D.

In my spare time I enjoy #birding, #DogTraining and #photography

I speack català, español, français & english

Reposting my #introduction so I can pin it - I'm a postdoctoral researcher doing #MicrobialEcology at Bangor University.

I'm interested in the interactions between #plants and their associated #microorganisms, particularly how they affect health outcomes in host plants, and how microbial communities differ on hosts across landscapes. I'm also interested in the question of how to accurately quantify and model microbial communities.

Keen on #rstats, #OpenScience, #BayesianStatistics, #Stan

Scientific pipeline from @rlmcelreath lecture 20 #OpenScience #BayesianStatistics