Ocean acoustic inversion with estimation of a posteriori probability distributions

1998 ◽  
Vol 104 (2) ◽  
pp. 808-819 ◽  
Author(s):  
Peter Gerstoft ◽  
Christoph F. Mecklenbräuker
Keyword(s):  
Probability Distributions ◽  
Posteriori Probability ◽  
A Posteriori ◽  
A Posteriori Probability ◽  
Acoustic Inversion

scholarly journals Evidence-based decision making and covid-19: what a posteriori probability distributions speak

2020 ◽  
Vol 3 (Special2) ◽  
pp. 286-292
Author(s):  
Sudhir Bhandari ◽  
Ajit Singh Shaktawat ◽  
Amit Tak ◽  
Jyotsna Shukla ◽  
Bhoopendra Patel ◽  
...  
Keyword(s):  
Decision Making ◽  
Goodness Of Fit ◽  
Probability Distributions ◽  
Age Distribution ◽  
Posteriori Probability ◽  
Evidence Based ◽  
A Posteriori ◽  
Bivariate Distributions ◽  
A Posteriori Probability ◽  
Evidence Based Decision Making

Background: In the absence of any pharmaceutical interventions, the management of the COVID-19 pandemic is based on public health measures. The present study fosters evidence-based decision making by estimating various “a posteriori probability distributions" from COVID-19 patients.  Methods: In this retrospective observational study, 987 RT-PCR positive COVID-19 patients from SMS Medical College, Jaipur, India, were enrolled after approval of the institutional ethics committee. The data regarding age, gender, and outcome were collected. The univariate and bivariate distributions of COVID-19 cases with respect to age, gender, and outcome were estimated. The age distribution of COVID-19 cases was compared with the general population's age distribution using the goodness of fit c2 test. The independence of attributes in bivariate distributions was evaluated using the chi-square test for independence. Results: The age group ‘25-29’ has shown highest probability of COVID-19 cases (P [25-29] = 0.14, 95% CI: 0.12- 0.16). The men (P [Male] = 0.62, 95%CI: 0.59-0.65) were dominant sufferers. The most common outcome was recovery (P [Recovered] = 0.79, 95%CI: 0.76-0.81) followed by admitted cases (P [Active]= 0.13, 95%CI: 0.11-0.15) and death (P [Death] = 0.08, 95%CI: 0.06-0.10). The age distribution of COVID-19 cases differs significantly from the age distribution of the general population (c2  =399.04, P < 0.001). The bivariate distribution of COVID-19 across age and outcome was not independent (c2 =106.21, df = 32, P < 0.001). Conclusion: The knowledge of disease frequency patterns helps in the optimum allocation of limited resources and manpower. The study provides information to various epidemiological models for further analysis.

scholarly journals Evidence based decision making and COVID-19: what a posteriori probability distributions speak.

2020 ◽  
Author(s):  
Sudhir Bhandari ◽  
Amit Tak ◽  
Jyotsna Shukla ◽  
Bhoopendra Patel ◽  
Ajit Singh Shaktawat ◽  
...  
Keyword(s):  
Decision Making ◽  
General Population ◽  
Probability Distributions ◽  
Age Distribution ◽  
Posteriori Probability ◽  
Evidence Based ◽  
A Posteriori ◽  
Epidemiological Models ◽  
Bivariate Distributions ◽  
Evidence Based Decision Making

Abstract Background: In the absence of a vaccine for coronavirus disease-19, the management of the current pandemic revolves around public health measures such as social distancing, lockdown, and contact tracing. A number of epidemiological models are used in decision making and for generating research intelligence. The models require information regarding the structures of social contact between different ages and genders. The present study fosters evidence-based decision making by estimating various a posteriori probability distributions from data of COVID-19 patients. Patients and Methods: In this retrospective observational study, 987 real-time RT-PCR SARS CoV-2 positive patients from SMS Medical College, Jaipur, India were enrolled after approval of the institutional ethics committee. The data regarding age, gender, and outcome were collected from case sheets. The univariate and bivariate distributions of COVID-19 cases with respect to age, gender, and outcome were estimated. The age distribution of COVID-19 cases was compared with the age distribution of general population using goodness of fit c2 test. The independence of attributes in bivariate distributions was evaluated using the chi square test for independence.Results: The age group ‘25-29’ has shown highest probability of COVID-19 cases (P[25-29] = 0.14, 95% CI: 0.12- 0.16). The men (P[Male] = 0.62, 95%CI: 0.59-0.65) were dominant sufferers. The most common outcome was recovery (P[Recovered] = 0.79, 95%CI: 0.76-0.81) followed by admitted cases (P[Active]= 0.13, 95%CI: 0.11-0.15) and death (P[Death] = 0.08, 95%CI: 0.06-0.10). The age distribution of COVID-19 cases differs significantly from the age distribution of the general population (c2 = 399.04, p < 0.001). The bivariate distribution of COVID-19 across age and outcome was not independent (c2 =106.21, df = 32, p < 0.001).Conclusion: The age, gender, and outcome distributions helps in evaluating disease dynamics and the social structure of the community. The knowledge of patterns of disease frequency helps in optimum allocation of limited resources and manpower. The study provides information for various epidemiological models, to decide the duration of lockdown.

Maximum a posteriori probability background estimation

1999 ◽  
Vol 105 (2) ◽  
pp. 1365-1366
Author(s):  
Thomas J. Green ◽  
William H. Payne ◽  
Vivian E. Titus ◽  
Eric J. Van Allen
Keyword(s):  
Posteriori Probability ◽  
Maximum A Posteriori ◽  
A Posteriori ◽  
A Posteriori Probability ◽  
Background Estimation
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