0 views collected around this technical thread.
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.
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 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 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.