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Bayesian inference

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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 IntelligenceBayesian inferenceIntent Prediction
0 likes · 11 min read
Can Robots Grasp Human Intentions? Theory of Mind Meets Bayesian Prediction
Model Perspective
Model Perspective
Oct 12, 2024 · Artificial Intelligence

From Deductive to Plausible Reasoning: How Bayesian Logic Shapes Everyday Decisions

Unlike strict deductive logic, plausible reasoning—grounded in evidence, experience, and probability—offers a practical way to draw conclusions under uncertainty, with applications ranging from medical diagnosis to daily choices and forming the mathematical basis of Bayesian inference that underpins modern AI systems.

Artificial IntelligenceBayesian inferencedecision making
0 likes · 7 min read
From Deductive to Plausible Reasoning: How Bayesian Logic Shapes Everyday Decisions
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.

Bayesian inferenceProphet 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 inferenceProbability Theorybeta distribution
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.

Bayesian inferencePythonmedical testing
0 likes · 6 min read
Unlocking Bayesian Methods: Theory, Real-World Examples, and Python Demo
Python Programming Learning Circle
Python Programming Learning Circle
Apr 24, 2023 · Artificial Intelligence

Implementing a Simple Probabilistic Programming Language in Python

This article explains the principles of probabilistic programming languages and walks through a step‑by‑step implementation of a minimal PPL in Python, covering model definition, variable representation, DAG traversal, log‑density computation, and a posterior grid illustration.

Bayesian inferenceDAGPPL
0 likes · 12 min read
Implementing a Simple Probabilistic Programming Language in Python
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.

Bayesian inferenceDAGPPL
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.

Bayesian inferenceGibbs samplingMCMC
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
Nov 12, 2022 · Fundamentals

Model-Centric Statistics: From Exploratory Analysis to Bayesian Inference

This article explains the fundamentals of statistics with a model‑centric approach, covering data collection, exploratory data analysis, descriptive statistics, visualization, and the three‑step Bayesian modeling process—including hypothesis formulation, model fitting, and evaluation—while emphasizing simplicity and practical programming tools such as Python.

Bayesian inferencePythondata analysis
0 likes · 6 min read
Model-Centric Statistics: From Exploratory Analysis to Bayesian Inference
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.

Bayesian inferenceHamiltonian Monte CarloMCMC
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.

Bayesian inferenceMCMCprobabilistic 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.

Bayesian inferencebeta distributionbinomial likelihood
0 likes · 8 min read
How Bayesian Inference Solves the Classic Coin Toss Problem
Model Perspective
Model Perspective
Oct 15, 2022 · Fundamentals

Understanding Probability Distributions: From Gaussian Curves to Bayesian Modeling

This article explains the concept of probability distributions, describes the Gaussian (normal) distribution with its parameters, demonstrates how to visualize it using Python code, and discusses random variables, independence, and real‑world examples such as atmospheric CO₂ time‑series data.

Bayesian inferenceGaussianProbability Distribution
0 likes · 6 min read
Understanding Probability Distributions: From Gaussian Curves to Bayesian Modeling
Model Perspective
Model Perspective
Oct 12, 2022 · Fundamentals

Mastering Model‑Centric Statistics: From Exploratory Analysis to Bayesian Inference

This article explains how statistics—through data collection, exploratory analysis, descriptive metrics, visualization, and model‑centric inference—provides a framework for understanding and predicting phenomena, emphasizing the role of programming (e.g., Python) and Bayesian modeling principles such as simplicity and the Occam razor.

Bayesian inferencedata scienceexploratory data analysis
0 likes · 6 min read
Mastering Model‑Centric Statistics: From Exploratory Analysis to Bayesian Inference
Model Perspective
Model Perspective
Sep 19, 2022 · Artificial Intelligence

Master Bayesian Linear Regression with PyMC3: A Hands‑On Guide

This tutorial explains how to use PyMC3 for Bayesian linear regression, covering model definition, data simulation, MAP estimation, advanced MCMC sampling with NUTS, and posterior analysis, all illustrated with complete Python code examples.

Bayesian inferenceMCMCPyMC3
0 likes · 11 min read
Master Bayesian Linear Regression with PyMC3: A Hands‑On Guide
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.

Bayesian inferenceMAP estimationMCMC
0 likes · 9 min read
How Bayesian Linear Regression Reveals Uncertainty in Model Parameters
IT Services Circle
IT Services Circle
Feb 8, 2022 · Artificial Intelligence

Implementing a Simple Probabilistic Programming Language (PPL) in Python

This article explains how probabilistic programming languages work and walks through building a minimal PPL in Python, covering model definition, latent and observed variables, DAG traversal, log‑density computation, and extensions such as posterior grid approximation.

Bayesian inferenceDAGPPL
0 likes · 11 min read
Implementing a Simple Probabilistic Programming Language (PPL) in Python
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.

Artificial IntelligenceBayesian inferenceFinance
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.

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