Browsing by Subject "Bayes"

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.