### Critique of the Bayesianist Medical Academic Establishment

The Bayesianist medical academic establishment has, in recent years, increasingly championed Bayesian methods for clinical prediction modeling and medical decision-making. While Bayesian approaches offer theoretical elegance and flexibility, their uncritical adoption and promotion—often without adequate consideration of their limitations—represent a significant threat to patient safety and scientific integrity.

**AI and Data Science Are Becoming Commoditized. What Should You Study in 2025 to Stay Ahead?**

The rapid commoditization of AI and data science tools means that technical skills alone are no longer enough to stand out. To future-proof your career, it’s time to go deeper and broader. Here’s what you should focus on in 2025:

1️⃣ **Math Fundamentals**:
- **Linear Algebra**: The backbone of machine learning and deep learning.
- **Probability**: Essential for understanding uncertainty and stochastic processes.

**Title: Bayesianism: The Ultimate Example of Academic Corruption** In the hallowed halls of academia, where the pursuit of truth is supposed to reign supreme, there exists a doctrine that has not only corrupted the scientific method but has also become a symbol of intellectual laziness and academic decadence.

While millions were taking Coursera courses and hearing, *"If you don’t understand it, it doesn’t matter,"* China’s elite coders were mastering an entirely different game. DeepSeek engineers, operating at an exceptional level, showcased x100 proficiency in both complex hardware and low-level programming.

Did you know that people tried to prove central limit theorem for over two centuries, first starting with de Moivre (1733), then almost a century after by Laplace who both used binomial distribution. Then it was Poisson who worked on this theorem, and Chebyshev (1890–1891) who gave a rigorous demonstration of it in the middle of the nineteenth century.

All you need is Kolmogorov–Arnold Network! 🔥🔥🔥 complete with GitHub repo 🚀🚀🚀🚀🚀

'KAN: Kolmogorov–Arnold Networks' from @MIT and @Caltech

h/t @illumattnati

code -> arxiv.org/abs/2404.19756
github.com/KindXiaoming/p…

Top 10 @github libraries for #conformalprediction Awesome Conformal Prediction - The most comprehensive professionally curated resource on Conformal Prediction by @predict_addict github.com/valeman/awesom…

Conformal prediction for #timeseries #forecasting is the hottest 🔥🔥🔥🔥 🚀🚀🚀🚀 area of research and applications right now.

The whole story started in summer 2021 when researchers from Georgia Tech published the first ever paper applying conformal prediction to time series. and presenting it at the first event at major conference totally dedicated to conformal prediction (ICML DFUQ 2021 organised by @ml_angelopoulos and @stats_stephen

@Zergylord @SchmidhuberAI Well unless you are top dog in DeepMind? Are you? @Zergylord @SchmidhuberAI Personally I find it hilarious that DeepMind is turning phd interns whilst some of its grunts can engage in trolling on Twitter during working time.

Motivated by having seen yet another Platt’s scaler post.

Platt’s scaling and isotonic regression are ~20 years old at this point. Both of them don’t have any mathematical guarantees of validity and are outperformed by conformal prediction Venn-ABERs

#conformalprediction VENN-ABERS is in fact a better regularised version of isotonic regression that constructors two isotonic regressions by postulating that a test object can a priori have both 0 and 1 as a label.

Another great development from @nixtlainc, ETS from StatsForecast is much more accurate and over 100 faster than NeuralProphet.

nice work @fede_gr and @nixtlainc team

#timeseries #forecasting Last year a few data scientists including myself exposed severe issues with facebook prophet resulting in facebook pulling down claims such as “with facebook prophet anyone can produce better forecasts than human experts “.