Browsing by Subject "Bayes"

The science of fish stock assessment is one that is very resource and labor intensive, with stock assessment models historically being based on data that causes a model to overestimate the strength of a population, sometimes with drastic consequences. The need of cost-effective assessment models and approaches increases, which is why I looked into using Bayesian modeling and networks as an approach not often used in fisheries science. I wanted to determine if it could be used to predict both recruitment and spawning stock biomass of four fish species in the north Atlantic, cod, haddock, pollock and capelin, based on no other evidence other than the recruitment or biomass data of the other species and if these results could be used to lower the uncertanties of fish stock models. I used data available on the RAM legacy database to produce four different models with the statistical software R, based on four different Bayes algorithms found in the R-package bnlearn, two based on continuous data and two based on discrete data. What I found was that there is much potential in the Bayesian approach to stock prediction and forecasting, as our prediction error percentage ranged between 1 and 40 percent. The best predictions were made when the species used as evidence had a high correlation coefficient with the target species, which was the case with cod and haddock biomass, which had a unusually high correlation of 0.96. As such, this approach could be used to make preliminary models of interactions between a high amount of species in a specific area, where there is data abundantly available and these models could be used to lower the uncertanties of the stock assessments. However, more research into the applicability for this approach to other species and areas needs to be conducted.

Official Statistics (OS) are crucial in facilitating informed and reliable decision-making. However, while the demand for diverse and precise information surges, challenges in obtaining accurate data emerge. The declining response rates to statistical surveys and escalating data collection costs further exacerbate the situation, particularly in surveys measuring rare events. This thesis explores the application of the Bayesian framework to statistical production. The concept of OS and the fundamental principles that guide their production are introduced. The suitability of the Bayesian approach for OS production is assessed from theoretical, philosophical, and practical standpoints. The core of statistical inference is explored, and the differences between Bayesian and frequentist approaches are compared. General tools for Bayesian inference and their practical utilization are presented, focusing especially on the graphical representation of a probabilistic model. Furthermore, a progressive construction of the proposed baseline model for analyzing Recreational Fishing Survey data is illustrated, with attention given to the issue of selection bias. The Bayesian Finnish Recreational Fishing Statistics 2020, with concise content produced through the developed model and the genuine data collected for OS, is also launched. While the thesis underscores that the proposed model should be regarded as a basis for further development, the results indicate that reasonable assessments can be obtained even with a simple Bayesian model. Overall, this thesis emphasizes the importance of adopting Bayesian thinking in statistical analysis to enhance knowledge-driven policy-making and adapt to the evolving information needs.