{"title":"POSTERIOR DE POLYA NO MONITORAMENTO AMOSTRAL DE PESCARIAS","authors":"Paul Gerhard Kinas, Jonata Cristian Wieczynski","doi":"10.28951/rbb.v38i2.441","DOIUrl":null,"url":null,"abstract":"A non-informative bayesian approach to sample-based fishery surveys is proposed. The Polya posterior for finite population parameters is used to obtain the inferences. The viability of a sampling plan was used in a pilot field experiment to collect weekly information about effort and catch from the artisanal fishery in Rio Grande, RS. Based on a simulated virtual population with four species and 345 fishermen, the sampling plan was tested using a sampling fraction of 3.3% from a complete data matrix of 2760 components. Results have shown accuracies above 71% for all but the most problematic species 2, and around 90% for estimates of total catch and cummulative effort. The percentile probability intervals (ICr) perform slightly better than the highest density interval (HDI) in terms of coverage; although both resulted about 5 percentage points bellow the nominal value of 95%.","PeriodicalId":36293,"journal":{"name":"Revista Brasileira de Biometria","volume":"11 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Revista Brasileira de Biometria","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.28951/rbb.v38i2.441","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Medicine","Score":null,"Total":0}
POSTERIOR DE POLYA NO MONITORAMENTO AMOSTRAL DE PESCARIAS
A non-informative bayesian approach to sample-based fishery surveys is proposed. The Polya posterior for finite population parameters is used to obtain the inferences. The viability of a sampling plan was used in a pilot field experiment to collect weekly information about effort and catch from the artisanal fishery in Rio Grande, RS. Based on a simulated virtual population with four species and 345 fishermen, the sampling plan was tested using a sampling fraction of 3.3% from a complete data matrix of 2760 components. Results have shown accuracies above 71% for all but the most problematic species 2, and around 90% for estimates of total catch and cummulative effort. The percentile probability intervals (ICr) perform slightly better than the highest density interval (HDI) in terms of coverage; although both resulted about 5 percentage points bellow the nominal value of 95%.