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Probability Calibration of Player Props: Brier and Log Loss | - Tipster & Co

Translated title. Original: Calibrazione Probabilità Player Props: Brier e Log Loss | - Tipster & Co

The document explains probability calibration for predictive models in betting, emphasizing that good ranking (discrimination) does not guarantee accurate probability estimates (calibration). It covers how to measure calibration, compare calibrated vs overconfident models using metrics like AUC, Brier score, log loss, and ECE, and describes constructing a reliability diagram to visualize calibration.

Introduction to the Probability Theory : Sochi, Taha: Amazon.de: Books

This introductory probability book combines notes, solved problems, exercises, and selected C++ examples for calculation and simulation. It is aimed at students with basic mathematics knowledge and can support courses, tutorials, or independent reading.

High-Dimensional Probability: An Introduction with Applications in Data Science (Cambridge Series in Statistical and Probabilistic Mathematics) : Vershynin, Roman: Amazon.de: Books

A second edition of High-Dimensional Probability offers a streamlined, self-contained introduction with new exercises and an added refresher chapter, aiming to serve as both a graduate textbook and a reference for researchers in probability, statistics, and data science. It emphasizes the connection between probability and high-dimensional geometry and is praised for its pedagogy and applicability to AI and machine learning.

Issue #143 - Understanding Logistic Regression: Sigmoid, Odds, and Thresholds

Logistic regression is explained as a probabilistic classifier using a linear combination of features and a sigmoid to map to probabilities; illustrated with a student-study example and discussed with equations and Python implementation.

Order statistic - Wikipedia

The document explains order statistics, including definitions of minimum, maximum, range, and median, and notes their relation to quantiles. It presents the probabilistic framework with the CDF and PDF of order statistics and mentions two special cases for the extreme order statistics.

Researchers Link Repeated Updates To Consistent Quantum Beliefs

Quantum beliefs evolve with repeated measurements; equivalence of different initial beliefs occurs when higher-order moments of their distributions match, enabling multi-shot prediction of future quantum states using a de Finetti framework. The work is theoretical and idealized, with future needs to address noise for real-world use.

Networks: Probability and Statistics (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 57) : Barbour, A. D., Reinert, Gesine: Amazon.de: Books

A book in the Cambridge Series on networks, probability, and statistics blends practical examples with rigorous probabilistic treatment of network models, offering accessible coverage of core methods and appendices, and is praised as a valuable resource for students and researchers in network science.

Perplexity adds real-time web rooting to its Decisions API – Unite.AI

Translated title. Original: Perplexity lägger till webbförankring i realtid i sitt Decisions API – Unite.AI

Perplexity adds real-time web grounding to its Decisions API, enabling live web search to inform probabilistic decisions. It reports improved accuracy with a modest latency increase and provides per-question pricing based on searches and token usage.

[2610.38304] Inner-Learning: From Hand-Coded Beliefs to Declarative Knowledge in Trick-Taking Games

The paper introduces a declarative belief-state model for trick-taking games that extracts hand-coded rules from a Skat engine into a new DSL, adds cardinality constraints and probabilistic updates, and demonstrates performance gains via self-play against the original engine.

[2611.04101] Structured Clinical Memory as Context for Probabilistic Decision Models: A Synthetic Evaluation with Jev

The study tests whether a compact structured memory improves a probabilistic decision model on synthetic clinical vignettes. Results show modest accuracy gains and better error metrics with memory, but the setup is synthetic and not clinically validated.