The article explores David Spiegelhalter's book on uncertainty, explaining how probability reflects personal ignorance, detailing Bayesian versus frequentist views, and illustrating real-world applications such as COVID risk communication, sports luck, and investment performance.
The author recounts a Phuket trip to see sea turtles and demonstrates how Bayesian updating, using prior guide data and new evidence such as season and weather, can estimate the probability of spotting turtles, illustrating the method with a concrete example.
This article examines the evolution of information security risk management—from classic standards like GB/T20984 and ISO27001 to modern AI‑driven, big‑data approaches—detailing risk definitions, quantitative models, international guidelines, and future research directions.
Bayesian thinking offers a powerful framework for updating beliefs by combining prior knowledge with new evidence, enabling more precise probability estimates and better decisions in uncertain, rapidly changing environments, illustrated through everyday examples like weather forecasts and cooking, and extending to fields such as medical diagnosis.
Bayesian hierarchical calibration applies a lightweight, interpretable Bayesian GLM with variational inference to correct pre‑ and post‑click scoring biases, using risk‑aware objectives that reduce calibration error by up to 66%, lift revenue by 5%, and cut conversion costs, while handling cold‑start and dimension‑wise sparsity in online advertising.
Exploring how probability quantifies uncertainty, the article examines questions like the chance of life on Mars, explains subjective versus objective interpretations, outlines Bayesian reasoning, Cromwell’s Rule, conditional probability, and why all probabilities can be viewed as conditional, linking logic and statistical inference.
This article explains the Bayesian theorem, introduces the Bayesian classifier, and details three Naive Bayes algorithms—Gaussian, Multinomial, and Bernoulli—along with their Scikit-learn implementations, key parameters, attributes, methods, and typical text‑classification applications for spam filtering.
The article examines Didi’s “Guess Where You’re Going” feature, describing its product benefits, the contextual data used, and a simple Bayesian Gaussian model that predicts a user’s destination based on time, departure location, and weekday, while also discussing its limitations and potential improvements.
This article explains how to create a functional spell‑checking tool using a 21‑line Python script that leverages Bayesian probability and a large word‑frequency corpus to suggest correct spellings for misspelled inputs.
This article explains why and how to discretize continuous features for categorical targets, compares three common discretization strategies, and details a Bayesian optimal method (MODL) with its mathematical formulation and implementation references.
This article demonstrates a compact 21‑line Python spell checker that uses a Bayesian model and edit‑distance concepts to suggest correct spellings, explaining the underlying probability theory and providing a full code walkthrough.
This article explains the fundamentals of Bayes' theorem, shows how to compute prior, likelihood, and posterior probabilities, demonstrates Bayesian A/B testing with Python code, introduces Bayesian networks for causal inference, and discusses the role of Bayesian methods in machine learning and data‑driven decision making.
Learn how to create a fully functional spell checker in just 21 lines of Python by training a Bayesian model on a large text corpus, generating candidate corrections via edit distance, and selecting the most probable word using probability calculations.
This article introduces the concepts of classification and prediction in data mining, outlines their workflow, and provides concise explanations of six widely used classification techniques—decision trees, K‑Nearest Neighbour, Support Vector Machine, Vector Space Model, Bayesian methods, and neural networks—highlighting their principles, advantages, and limitations.
