Aleatoric and epistemic uncertainty in machine learning: an ...
Mar 8, 2021 · Machine learning methods for probability estimation (Sect. 4.1), i.e., for training a probabilistic predictor, often commit to a single hypothesis learned on the data, thereby …
Representations of Uncertainty in Artificial Intelligence: Probability …
May 8, 2020 · The aim of this chapter is to provide an introductive survey that lays bare specific features of two basic frameworks for representing uncertainty: probability theory and possibility …
A Gentle Introduction to Uncertainty in Machine Learning
Sep 25, 2019 · Noise in data, incomplete coverage of the domain, and imperfect models provide the three main sources of uncertainty in machine learning. Probability provides the foundation …
Uncertainty in Machine Learning Idea: The ML model should output a prediction and the corresponding uncertainty. The uncertainty indicates the probable interval within which an …
Probability Theory in Machine Learning - GeeksforGeeks
May 8, 2025 · where 𝑧 is the critical value from the standard normal distribution. For example, if a machine learning model predicts the weight of a watermelon to be 5 kg, with a confidence …
Understanding Probability Distributions in Machine Learning
Jan 20, 2024 · Understanding probability distributions is a fundamental aspect of machine learning and statistics. They provide a mathematical framework for modeling uncertainty and …
Representing uncertainty and imprecision in machine learning: …
Jan 1, 2024 · On this basis, we survey the existing TBF-based methods in detail and explain how to characterize uncertainty and imprecision in the results. What is more, we discuss the …
The aim of this chapter is to provide an introductive survey that lays bare specific features of two basic frame- works for representing uncertainty: probability theory and possibility theory, while …
A Comprehensive Guide to Model Uncertainty in Machine Learning …
Sep 1, 2022 · There are two sources of uncertainty that affect machine learning algorithms, epistemic and aleatoric. Predictive uncertainty is also a useful notion that allows us to quantify …
Probability Distributions in Machine Learning - Data Science
Dec 25, 2022 · Probability distributions form the foundation of statistical modeling and machine learning, enabling the representation and analysis of uncertainty in data. These distributions …