Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 77 tok/s
Gemini 2.5 Pro 54 tok/s Pro
GPT-5 Medium 29 tok/s Pro
GPT-5 High 26 tok/s Pro
GPT-4o 103 tok/s Pro
Kimi K2 175 tok/s Pro
GPT OSS 120B 454 tok/s Pro
Claude Sonnet 4.5 38 tok/s Pro
2000 character limit reached

Binned Likelihood including Monte Carlo Statistical Uncertainty in Bayesian Inference (2304.05433v2)

Published 11 Apr 2023 in hep-ex and physics.data-an

Abstract: Data analysis in HEP experiments often uses binned likelihood from data and finite Monte Carlo sample. Statistical uncertainty of Monte Carlo sample has been introduced in Frequentist Inference in some literatures, but they are not suitable for Bayesian Inference. This technical note introduces the binned likelihood with Monte Carlo statistical uncertainty in Bayesian Inference and includes the derivation of it. It turns out that the results are similar to the results in [1]. But this tech-note gives an alternate and more intuitive derivation of the content

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Authors (2)

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube