@inbook{3c585bf0ee09494abfd56b4d5ab7243b,
title = "Possibility-probability relation in medical models",
abstract = "Medical models based on possibility theory are usually much simpler than those based on probability theory, but they lack foundations. The most foundational question is: How to obtain a possibility distribution for a modeling process? As one of the solutions, a simple framework is proposed built on the conjecture that a probability distribution for an uncertain process can be predicted by the process's possibility distribution. The case of the perfect prediction and the case of possibility and probability distributions related less than perfect (modeling {"}ideal body weight{"}) are considered in this paper.",
keywords = "Possibilistic modeling in medicine and biology, Possibility distribution function, Possibility theory, Probability, Probability density function",
author = "A. Bolotin",
year = "2003",
month = jan,
day = "1",
doi = "10.1007/978-3-540-39619-2_1",
language = "English",
isbn = "354020282X",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "1--8",
editor = "Petra Perner and Rudiger Brause and Hermann-Georg Holzhutter",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "Germany",
}