scholarly journals Bayesian probability estimates are not necessary to make choices satisfying Bayes’ rule in elementary situations

2015 ◽  
Vol 6 ◽  
Author(s):  
Artur Domurat ◽  
Olga Kowalczuk ◽  
Katarzyna Idzikowska ◽  
Zuzanna Borzymowska ◽  
Marta Nowak-Przygodzka
Keyword(s):  
Bayesian Probability ◽  
Bayes Rule ◽  
Probability Estimates

The Spirit of Cromwell’s Rule

2018 ◽  
Author(s):  
Timothy McGrew
Keyword(s):  
Bayesian Methods ◽  
Bayesian Model ◽  
Bayesian Probability ◽  
Standard Textbook ◽  
Per Se

One of the central complaints about Bayesian probability is that it places no constraints on individual subjectivity in one’s initial probability assignments. Those sympathetic to Bayesian methods have responded by adding restrictions motivated by broader epistemic concerns about the possibility of changing one’s mind. This chapter explores some cases where, intuitively, a straightforward Bayesian model yields unreasonable results. Problems arise in these cases not because there is something wrong with the Bayesian formalism per se but because standard textbook illustrations teach us to represent our inferences in simplified ways that break down in extreme cases. It also explores some interesting limitations on the extent to which successive items of evidence ought to induce us to change our minds when certain screening conditions obtain.

Probability

2017 ◽  
Author(s):  
Andrew Gelman ◽  
Deborah Nolan
Keyword(s):  
Conditional Probability ◽  
Bayes Rule ◽  
Probability Models ◽  
Classroom Activities ◽  
Basic Probability

This chapter contains many classroom activities and demonstrations to help students understand basic probability calculations, including conditional probability and Bayes rule. Many of the activities alert students to misconceptions about randomness. They create dramatic settings where the instructor discerns real coin flips from fake ones, students modify dice and coins in order to load them, students “accused” of lying based on the outcome of an inaccurate simulated lie detector face their classmates. Additionally, probability models of real outcomes offer good value: first we can do the probability calculations, and then can go back and discuss the potential flaws of the model.

scholarly journals Using body temperature and variables commonly available in the EHR to predict acute infection: a proof-of-concept study showing improved pretest probability estimates for acute COVID-19 infection among discharged emergency department patients

Diagnosis ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Carl T. Berdahl ◽  
An T. Nguyen ◽  
Marcio A. Diniz ◽  
Andrew J. Henreid ◽  
Teryl K. Nuckols ◽  
...  
Keyword(s):  
Emergency Department ◽  
Body Temperature ◽  
Acute Infection ◽  
Time Of Day ◽  
Pretest Probability ◽  
Absolute Difference ◽  
Proof Of Concept ◽  
Easy Method ◽  
Concept Study ◽  
Probability Estimates

Abstract Objectives Obtaining body temperature is a quick and easy method to screen for acute infection such as COVID-19. Currently, the predictive value of body temperature for acute infection is inhibited by failure to account for other readily available variables that affect temperature values. In this proof-of-concept study, we sought to improve COVID-19 pretest probability estimation by incorporating covariates known to be associated with body temperature, including patient age, sex, comorbidities, month, and time of day. Methods For patients discharged from an academic hospital emergency department after testing for COVID-19 in March and April of 2020, we abstracted clinical data. We reviewed physician documentation to retrospectively generate estimates of pretest probability for COVID-19. Using patients’ COVID-19 PCR test results as a gold standard, we compared AUCs of logistic regression models predicting COVID-19 positivity that used: (1) body temperature alone; (2) body temperature and pretest probability; (3) body temperature, pretest probability, and body temperature-relevant covariates. Calibration plots and bootstrap validation were used to assess predictive performance for model #3. Results Data from 117 patients were included. The models’ AUCs were: (1) 0.69 (2) 0.72, and (3) 0.76, respectively. The absolute difference in AUC was 0.029 (95% CI −0.057 to 0.114, p=0.25) between model 2 and 1 and 0.038 (95% CI −0.021 to 0.097, p=0.10) between model 3 and 2. Conclusions By incorporating covariates known to affect body temperature, we demonstrated improved pretest probability estimates of acute COVID-19 infection. Future work should be undertaken to further develop and validate our model in a larger, multi-institutional sample.

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