Search Articles — Sudonull

https://sudonull.com/hyperloglog-and-count-min-sketch-in-go-for-big-data

Implementation of HyperLogLog and Count-Min Sketch in Go: code, benchmarks, comparison. Estimating uniqueness and frequencies with minimal memory for data streams. For middle/senior devs.

https://sudonull.com/deterministic-ai-validation-end-of-the-gpu-race

Learn why probabilistic AI models are inefficient and how a deterministic firewall on WebAssembly solves hallucinations. ASIC vs GPU benchmarks: 400x energy savings. For developers.

https://sudonull.com/modeling-luck-in-it-career

Learn how probabilistic models explain the role of randomness in developers' success. Simulations of asymmetric systems, threshold effect, and adaptability strategies. Read the analysis for middle/senior.

https://sudonull.com/clickhouse-aggregate-functions-uniqhll12-quantiletdigest-topk

uniqHLL12 for fast unique users, quantileTDigest for distributions, groupArray, topK, -If combinators. Examples of GGR, cohorts, retention.

Sep 27, 2018 ·

https://arxiv.org/abs/1809.10756

Sep 27, 2018 · This book is a graduate-level introduction to probabilistic programming . It not only provides a thorough background for anyone wishing to use a probabilistic programming system, but also introduces the techniques needed to design and build these systems. It is aimed at people who have an undergraduate-level understanding of either or, ideally, both probabilistic machine learning and ...

Jul 7, 2020 ·

https://towardsdatascience.com/intro-to-probabilistic-programming-b47c4e926ec5/

Jul 7, 2020 · Probabilistic programming is about doing statistics using the tools of computer science. On Tensorflow probability In the above figure you can see a typical computer science programming pipeline: Write a program , specify the values of its arguments then evaluate the program to produce an output.

Dec 9, 2025 ·

https://www.coursera.org/articles/probabilistic-programming

Dec 9, 2025 · Probabilistic programming ’s roots come from Bayesian programming , which dynamically updates a statistical hypothesis using Bayes' Theorem. One of the core concepts of probabilistic programming is the use of latent random variables, as using latent, or unobserved, variables makes reasoning about a problem easier.

Searches related to probabilistic programming

https://simons.berkeley.edu/sites/default/files/docs/5675/talkprintversion.pdf

The goal of probabilistic programming is to enable probabilistic modeling and machine learning to be accessible to the working programmer, who has su cient domain expertise, but perhaps not enough expertise in probability theory or machine learning. We wish to hide the details of inference inside the compiler and run-time, and enable the programmer to express models using her domain expertise ...