Deep Dive into Forward vs Reverse KL Divergence: When to Use Which?

The article explains the definitions, properties, and asymmetric nature of KL divergence, compares Forward KL (mean‑seeking) and Reverse KL (mode‑seeking) through bimodal examples, and provides practical guidelines for choosing between them based on sampling and probability‑evaluation capabilities in machine‑learning tasks.

Forward KLKL divergenceModel Selection

0 likes · 10 min read

Deep Dive into Forward vs Reverse KL Divergence: When to Use Which?

Machine Heart

Mar 31, 2026 · Artificial Intelligence

Can LLM Judges Be Trusted? TrustJudge Leverages Full Probability Distributions

LLM judges often produce contradictory scores and non‑transitive preferences; the TrustJudge framework replaces discrete scoring with distribution‑sensitive scoring and likelihood‑aware aggregation, dramatically reducing both score‑comparison and pairwise‑transitivity inconsistencies across multiple model families, improving accuracy and even serving as a reward signal for RL training.

LLM evaluationReward ModelingTrustJudge

0 likes · 12 min read

Can LLM Judges Be Trusted? TrustJudge Leverages Full Probability Distributions

Data Party THU

Oct 5, 2025 · Fundamentals

Which Probability Distribution Fits Your Data? A Practical Guide to 8 Core Models

This article presents eight essential probability distributions for everyday data‑science tasks, explains when to use each, provides concise Python code for fitting and sampling, and shares practical tips and a real‑world case study to help you choose the right model quickly.

Statistical Modelingdata analysisprobability distribution

0 likes · 11 min read

Which Probability Distribution Fits Your Data? A Practical Guide to 8 Core Models

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 inferenceGaussianPython visualization

0 likes · 6 min read

Understanding Probability Distributions: From Gaussian Curves to Bayesian Modeling