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Model Perspective
Model Perspective
May 27, 2026 · Product Management

Decoding “All Good” Signals: Principal‑Agent Theory & Bayesian Inference

The article explains how everyday workplace interactions can be modeled with principal‑agent theory and Bayesian updating, showing how to infer a manager’s hidden intentions from explicit and implicit signals, illustrated by a product‑manager case study and practical guidelines for building priors, handling noise, and avoiding bias.

bayesian inferencedecision makingmanager intent
0 likes · 10 min read
Decoding “All Good” Signals: Principal‑Agent Theory & Bayesian Inference
Model Perspective
Model Perspective
May 19, 2026 · Fundamentals

Which of the Three Logics Do You Use in Everyday Reasoning?

The article explores Joseph Mazur’s three kinds of logic—classical, infinite, and plausible reasoning—detailing their historical origins, formal representations, real‑world examples, and how confusing them can lead to faulty judgments.

bayesian inferenceclassical logicinfinite logic
0 likes · 11 min read
Which of the Three Logics Do You Use in Everyday Reasoning?
Model Perspective
Model Perspective
Oct 7, 2025 · Fundamentals

Unlock Life’s Success: The Three Powers of Cognition, Choice & Growth

This article treats life as an optimization problem and breaks it into three core forces—cognition, choice, and growth—showing how Bayesian inference, multi‑objective optimization, and dynamic system theory can model their interactions, guide decision‑making, and illustrate the feedback loops that drive personal development.

OptimizationPersonal Developmentbayesian inference
0 likes · 8 min read
Unlock Life’s Success: The Three Powers of Cognition, Choice & Growth
Model Perspective
Model Perspective
Mar 30, 2025 · Artificial Intelligence

Can Robots Grasp Human Intentions? Theory of Mind Meets Bayesian Prediction

This article explores how understanding others' mental states—from basic intentions to recursive mindreading—can be modeled with Bayesian inference and applied to robots for predicting human behavior in scenarios like pedestrian crossing, shopping assistance, and multi‑agent games.

Artificial IntelligenceIntent PredictionMulti-Agent Systems
0 likes · 11 min read
Can Robots Grasp Human Intentions? Theory of Mind Meets Bayesian Prediction
DeWu Technology
DeWu Technology
May 31, 2024 · Artificial Intelligence

In-depth Analysis of Prophet Time Series Forecasting Model

The article offers a thorough examination of Facebook’s Prophet forecasting model, detailing its additive decomposition of trend, seasonality, holidays and regressors, the underlying Bayesian inference via Stan, the full training‑and‑prediction pipeline, data‑normalization tricks, uncertainty estimation, and practical source‑code insights for e‑commerce applications.

Machine LearningProphet modelStan framework
0 likes · 21 min read
In-depth Analysis of Prophet Time Series Forecasting Model
Model Perspective
Model Perspective
Aug 16, 2023 · Fundamentals

Understanding the Beta Distribution: A Key to Bayesian Inference

This article explores the Beta distribution’s role in Bayesian statistics, detailing its definition, properties, conjugate prior relationship, and practical examples such as coin flips and bus arrivals, illustrating how it simplifies probability updates and supports intuitive belief revision.

bayesian inferencebeta distributionconjugate prior
0 likes · 10 min read
Understanding the Beta Distribution: A Key to Bayesian Inference
Model Perspective
Model Perspective
Aug 12, 2023 · Fundamentals

Unlocking Bayesian Methods: Theory, Real-World Examples, and Python Demo

This article explains Bayesian methods—its core theorem, historical and everyday applications, a detailed medical testing model, and provides a step‑by‑step Python calculation illustrating how prior probabilities and new evidence combine to produce posterior probabilities.

Medical TestingPythonbayesian inference
0 likes · 6 min read
Unlocking Bayesian Methods: Theory, Real-World Examples, and Python Demo
Python Programming Learning Circle
Python Programming Learning Circle
Feb 17, 2023 · Artificial Intelligence

Building a Simple Probabilistic Programming Language in Python

This article explains the principles of probabilistic programming languages and walks through constructing a basic PPL in Python, covering model definition with latent and observed variables, distribution handling, DAG traversal for log‑density computation, and demonstrates evaluation with example code and visualizations.

DAGPPLPython
0 likes · 13 min read
Building a Simple Probabilistic Programming Language in Python
Model Perspective
Model Perspective
Nov 29, 2022 · Artificial Intelligence

MCMC Demystified: Monte Carlo Basics, Metropolis-Hastings & Gibbs Sampling

Markov Chain Monte Carlo (MCMC) extends classic Monte Carlo by generating dependent samples via a Markov chain, enabling Bayesian inference through concepts like the plug‑in principle, burn‑in, asymptotic independence, and algorithms such as Metropolis‑Hastings and Gibbs sampling, while addressing convergence and effective sample size.

Gibbs samplingMCMCMetropolis-Hastings
0 likes · 13 min read
MCMC Demystified: Monte Carlo Basics, Metropolis-Hastings & Gibbs Sampling
Model Perspective
Model Perspective
Nov 27, 2022 · Fundamentals

How Bayesian Phylogenetics Uncovers the Evolution and Spread of Fast‑Evolving Viruses

This review outlines modern Bayesian phylogenetic methods for reconstructing the origins, timing, and population dynamics of rapidly evolving RNA viruses such as HIV, HCV, and influenza, highlighting coalescent theory, relaxed molecular clocks, and the integration of epidemiological models with genetic data.

bayesian inferencecoalescent theoryepidemiology
0 likes · 38 min read
How Bayesian Phylogenetics Uncovers the Evolution and Spread of Fast‑Evolving Viruses
Model Perspective
Model Perspective
Oct 22, 2022 · Fundamentals

Unlocking Bayesian Sampling: How MCMC and Hamiltonian Monte Carlo Work

This article explains the principles behind Markov Chain Monte Carlo methods, including Monte Carlo sampling, the Metropolis‑Hastings algorithm, and the Hamiltonian Monte Carlo (HMC) approach, illustrating how they efficiently approximate posterior distributions in Bayesian analysis.

Hamiltonian Monte CarloMCMCMetropolis-Hastings
0 likes · 11 min read
Unlocking Bayesian Sampling: How MCMC and Hamiltonian Monte Carlo Work
Model Perspective
Model Perspective
Oct 20, 2022 · Artificial Intelligence

Unlocking Bayesian Inference: How Probabilistic Programming Simplifies Complex Models

This article explains Bayesian statistics as a probabilistic framework, describes how modern numerical methods and probabilistic programming languages automate inference, and reviews both Markov and non‑Markov techniques such as MCMC, grid computation, Laplace approximation, and variational inference for building complex models.

MCMCbayesian inferenceprobabilistic programming
0 likes · 7 min read
Unlocking Bayesian Inference: How Probabilistic Programming Simplifies Complex Models
Model Perspective
Model Perspective
Oct 18, 2022 · Fundamentals

How Bayesian Inference Solves the Classic Coin Toss Problem

This article introduces Bayesian inference through the classic coin‑toss example, explaining how to model bias with a beta prior and binomial likelihood, derive the posterior distribution, and understand convergence of different priors as data accumulates.

Coin Tossbayesian inferencebeta distribution
0 likes · 8 min read
How Bayesian Inference Solves the Classic Coin Toss Problem
Model Perspective
Model Perspective
Sep 18, 2022 · Artificial Intelligence

How Bayesian Linear Regression Reveals Uncertainty in Model Parameters

This article explains Bayesian linear regression, describing its probabilistic treatment of weights, prior and posterior computation, MAP and numerical solutions, and how it enables uncertainty quantification, online learning, and model comparison through Bayes factors.

MAP estimationMCMCMachine Learning
0 likes · 9 min read
How Bayesian Linear Regression Reveals Uncertainty in Model Parameters
Code DAO
Code DAO
May 7, 2022 · Artificial Intelligence

Why Normal (Gaussian) Distributions Are Fundamental to Machine Learning

The article explains how normal (Gaussian) distributions underpin many machine‑learning algorithms, reviewing the central limit theorem, multivariate Gaussian sampling, and key properties such as products, sums, conditional and marginal distributions, linear transformations, and Gaussian‑based Bayesian inference.

GaussianMachine Learningbayesian inference
0 likes · 7 min read
Why Normal (Gaussian) Distributions Are Fundamental to Machine Learning
ITPUB
ITPUB
May 17, 2021 · Operations

How DBSCAN Clustering and Bayesian Inference Boost Root‑Cause Detection in Securities Trading Systems

This article describes how a Chinese securities firm applied big‑data‑driven clustering and Bayesian methods to automate root‑cause analysis of trading‑system anomalies, detailing the challenges, algorithmic designs, practical implementations, and evaluation results that demonstrate significant reductions in false alarms and faster recovery.

MonitoringOperationsRoot Cause Analysis
0 likes · 17 min read
How DBSCAN Clustering and Bayesian Inference Boost Root‑Cause Detection in Securities Trading Systems
dbaplus Community
dbaplus Community
May 16, 2021 · Operations

How DBSCAN Clustering and Bayesian Inference Enable Fast Root‑Cause Detection in Securities Trading Systems

This article details the challenges of root‑cause identification in high‑availability securities trading platforms and presents two intelligent‑operations solutions—DBSCAN‑based clustering and Bayesian inference—to quickly locate anomalies and improve recovery efficiency.

Anomaly DetectionDBSCANIntelligent Operations
0 likes · 17 min read
How DBSCAN Clustering and Bayesian Inference Enable Fast Root‑Cause Detection in Securities Trading Systems
AntTech
AntTech
Jun 10, 2019 · Artificial Intelligence

Ant Financial AI Advances Presented at ICML 2019

The article reports on Ant Financial’s participation in ICML 2019, highlighting a finance‑focused AI workshop and summarizing three of its cutting‑edge research papers on adversarial reinforcement‑learning recommendation, distributional gradient temporal‑difference learning, and particle‑flow Bayesian inference.

Financebayesian inference
0 likes · 5 min read
Ant Financial AI Advances Presented at ICML 2019
Architects Research Society
Architects Research Society
Oct 4, 2015 · Artificial Intelligence

Bayesian Thinking on Your Feet: Embedding Generative Models in Reinforcement Learning for Sequentially Revealed Data

This NSF‑funded project aims to develop algorithms that incrementally process partially observed data, integrating generative models with reinforcement‑learning policies to decide when to act, applied to simultaneous machine translation and quiz‑bowl style question answering.

Generative Modelsbayesian inferencemachine translation
0 likes · 4 min read
Bayesian Thinking on Your Feet: Embedding Generative Models in Reinforcement Learning for Sequentially Revealed Data