Tagged articles

Parameter Estimation

11 articles · Page 1 of 1

Jul 24, 2026 · Fundamentals

Maximum Likelihood Estimation Explained Simply: Core Principles, Code Walkthroughs, and Limitations

This article demystifies maximum likelihood estimation by using a coin‑flip example to contrast probability and likelihood, introduces log‑likelihood for numerical stability, shows Python implementations for discrete and continuous cases, discusses historical origins, and highlights practical limitations such as small‑sample bias and over‑fitting.

Log-LikelihoodMaximum Likelihood EstimationParameter Estimation

0 likes · 30 min read

Maximum Likelihood Estimation Explained Simply: Core Principles, Code Walkthroughs, and Limitations

AI Explorer

May 2, 2026 · Artificial Intelligence

How a New AI Probe Can Reverse‑Engineer LLM Parameter Counts

Researcher Li Bojie’s “Uncompressible Knowledge Probe” uses random, black‑box API queries to gauge how much irreducible knowledge a large language model retains, allowing an indirect estimate of its effective parameter count and prompting a broader debate on model evaluation and transparency.

AI evaluationLLMParameter Estimation

0 likes · 5 min read

How a New AI Probe Can Reverse‑Engineer LLM Parameter Counts

Machine Heart

May 1, 2026 · Artificial Intelligence

API‑Only Probes Reveal GPT, Claude, Gemini Parameter Counts – Community Buzz

A new arXiv paper introduces Incompressible Knowledge Probes that estimate large language model sizes via black‑box API calls, fitting a log‑linear relation on 89 open‑source models and producing controversial parameter estimates for GPT‑5.5, Claude Opus, Gemini and others, sparking heated community debate.

AI scalingClaude OpusGPT-5.5

0 likes · 7 min read

API‑Only Probes Reveal GPT, Claude, Gemini Parameter Counts – Community Buzz

Meituan Technology Team

May 22, 2025 · Fundamentals

Unlocking AB Testing: Core Statistical Principles Behind Reliable Experiments

This article explains the statistical foundations of AB testing, covering the Rubin causal model, SUTVA and randomization assumptions, parameter and confidence‑interval estimation, hypothesis‑testing procedures, and essential limit theorems such as the law of large numbers and the central limit theorem.

AB testingParameter Estimationcausal inference

0 likes · 17 min read

Unlocking AB Testing: Core Statistical Principles Behind Reliable Experiments

Model Perspective

May 8, 2024 · Fundamentals

Master Mathematical Modeling: The Three Essential Pillars of Elements, Structure, and Parameters

This article explains how successful mathematical modeling relies on three core components—identifying essential variables, designing an appropriate structural framework, and accurately setting parameter values—to transform real‑world problems into reliable, actionable mathematical representations.

Parameter Estimationmathematical modelingmodel elements

0 likes · 8 min read

Master Mathematical Modeling: The Three Essential Pillars of Elements, Structure, and Parameters

Model Perspective

Mar 1, 2023 · Artificial Intelligence

Mastering the EM Algorithm: Theory, Math, and Python Implementation

This article explains the Expectation‑Maximization (EM) algorithm, detailing its iterative E‑step and M‑step processes, mathematical formulation, and practical Python implementation for estimating parameters of mixed linear regression models, while highlighting convergence considerations and common pitfalls.

EM algorithmParameter EstimationPython

0 likes · 12 min read

Mastering the EM Algorithm: Theory, Math, and Python Implementation

Model Perspective

Nov 12, 2022 · Fundamentals

Understanding Parameter Estimation: Point vs Interval Methods

This article explains statistical inference focusing on parameter estimation, distinguishing point estimation from interval estimation, and demonstrates how to construct confidence intervals for population means using sample data, including a practical example calculating a confidence interval for the average Sharpe ratio of equity funds.

Parameter Estimationconfidence intervalinterval estimation

0 likes · 3 min read

Understanding Parameter Estimation: Point vs Interval Methods

Model Perspective

Nov 5, 2022 · Fundamentals

Mastering ARMA: Build and Forecast Time Series Models with AIC and Python

This article explains how to identify, order‑select, estimate parameters, validate, and forecast ARMA time‑series models, covering the Akaike Information Criterion, various estimation techniques, and diagnostic tests such as the Ljung‑Box test, with practical Python implementation guidance.

AICARMAParameter Estimation

0 likes · 4 min read

Mastering ARMA: Build and Forecast Time Series Models with AIC and Python

Model Perspective

Oct 31, 2022 · Fundamentals

Mastering the Method of Moments: Theory and Python Example

This article explains the method of moments for estimating population parameters, outlines its step‑by‑step derivation, and demonstrates a Python implementation that estimates a basketball player's shooting odds from binary outcome data using.

Data AnalysisParameter EstimationPython

0 likes · 4 min read

Mastering the Method of Moments: Theory and Python Example

Model Perspective

Oct 29, 2022 · Fundamentals

Understanding Parameter Estimation: Point vs Interval and Confidence Intervals

This article explains statistical inference focusing on parameter estimation, distinguishing point and interval estimates, describing how confidence levels relate to significance levels, and illustrating the calculation of confidence intervals for population means with a practical example using Sharpe ratios from a sample of 100 observations.

Parameter EstimationSharpe ratioconfidence interval

0 likes · 3 min read

Understanding Parameter Estimation: Point vs Interval and Confidence Intervals

Model Perspective

Jul 19, 2022 · Fundamentals

How to Model Drug Distribution and Elimination Using a Two‑Compartment Pharmacokinetic Model

This article explains the assumptions, equations, and parameter‑estimation methods of a two‑compartment pharmacokinetic model for describing drug distribution, transfer between central and peripheral spaces, and elimination, and compares common dosing routes such as rapid IV bolus, constant‑rate infusion, and oral or intramuscular administration.

Parameter Estimationdosing methodsdrug distribution

0 likes · 2 min read

How to Model Drug Distribution and Elimination Using a Two‑Compartment Pharmacokinetic Model