Binary probability model

Clinical prediction models providing binary categorizations for clinical decision support require the selection of a probability threshold, or “cutpoint,” to classify individuals. Existing cutpoint selection approaches typically optimize test-specific metrics, including sensitivity and specificity, but overlook the consequences of correct or incorrect classification.

11p vighostrider 25-05-2023 3 2   Download

This paper is to analyse the poverty reduction results, clarify some basic factors and their impacts on poverty probability in Hoi An city by using quantitative methods: The model of Binary Logistic regression.

4p vilexus 30-09-2022 41 4   Download

Triflusal is a drug that inhibits platelet aggregation. In this study we investigated the dose-exposureresponse relationship of a triflusal formulation by population pharmacokinetic (PK) and pharmacodynamic (PD) modeling of its main active metabolite, hydroxy-4-(trifluoromethyl) benzoic acid (HTB).

10p vienzym2711 03-04-2020 7 2   Download

In this paper, we propose a new probabilistic relational database model, denoted by PRDB, as an extension of the classical relational database model where the uncertainty of relational attribute values and tuples are respectively represented by finite sets and probability intervals. A probabilistic interpretation of binary relations on finite sets is proposed for the computation of their probability measures. The combination strategies on probability intervals are employed to combine attribute values and compute uncertain membership degrees of tuples in a relation.

18p 12120609 23-03-2020 25 1   Download

Lecture "Advanced Econometrics (Part II) - Chapter 3: Discrete choice analysis - Binary outcome models" presentation of content: Discrete choice model, basic types of discrete values, the probability models, estimation and inference in binary choice model, binary choice models for panel data.

18p nghe123 06-05-2016 59 6   Download

The paper presents a language model that develops syntactic structure and uses it to extract meaningful information from the word history, thus enabling the use of long distance dependencies. The model assigns probability to every joint sequence of words-binary-parse-structure with headword annotation. The model, its probabilistic parametrization, and a set of experiments meant to evaluate its predictive power are presented.

3p bunthai_1 06-05-2013 34 1   Download

We investigate generalizations of the allsubtrees "DOP" approach to unsupervised parsing. Unsupervised DOP models assign all possible binary trees to a set of sentences and next use (a large random subset of) all subtrees from these binary trees to compute the most probable parse trees. We will test both a relative frequency estimator for unsupervised DOP and a maximum likelihood estimator which is known to be statistically consistent. We report state-ofthe-art results on English (WSJ), German (NEGRA) and Chinese (CTB) data. ...

8p hongvang_1 16-04-2013 48 1   Download