Research Topic · Peer-Reviewed

Probability Estimation

Probability estimation is a process used in statistics and machine learning to estimate the likelihood of an event occurring. It is typically used for predicting future outcomes and to make decisions about which action to take. Probability estimation can be used for a wide variety of applications, from predicting ma…

Curated from this journal's research 📚 1 peer-reviewed article cited 🔖 ISSN 2643-2811 🗓 Reviewed June 2026

Overview

Probability estimation is a process used in statistics and machine learning to estimate the likelihood of an event occurring. It is typically used for predicting future outcomes and to make decisions about which action to take. Probability estimation can be used for a wide variety of applications, from predicting market trends to identifying disease outbreaks. It can also be used to simulate complex systems in order to improve accuracy and efficiency in decision-making. By using probability estimation to accurately weigh the risks associated with specific decisions, it can help to improve decision-making outcomes and increase overall effectiveness.

Research published in this journal

1 peer-reviewed article, ranked by relevance. Each links to its DOI.

Editorial oversight

Curated from peer-reviewed research published in Model Based Research (ISSN 2643-2811).

Journal editorial board
Yoshiaki Kikuchi · Japan Yung-Yao Chen · Taiwan Yang Chen · United States

This page summarises published research for orientation; it is not medical or professional advice.