arXiv subjects

Samu Mäntyniemi

Publications and source records attributed to Samu Mäntyniemi.

3 recordsLinked to original sources

arXiv2017

Optimal design of observational studies: overview and synthesis

We review typical design problems encountered in the planning of observational studies and propose a unifying framework that allows us to use the same concepts and notation for different problems. In the framework, the design is defined as a probability measure in the space of observational processes that determine whether the value of a variable is observed for a specific unit at the given time. The optimal design is then defined, according to Bayesian decision theory, to be the one that maximizes the expected utility related to the design. We present examples on the use of the framework and discuss methods for deriving optimal or approximately optimal designs.

math.ST↗

arXiv2015

A Bayesian length-based population dynamics model for northern shrimp (Pandalus Borealis)

We introduce a fully length-based Bayesian model for the population dynamics of northern shrimp (Pandalus Borealis). This has the advantage of structuring the population in terms of a directly observable quantity, requiring no indirect estimation of age distributions from measurements of size. The introduced model is intended as a simplistic prototype around which further developments and refinements can be built. As a case study, we use the model to analyze the population of Skagerrak and the Norwegian Deep in the years 1988-2012.

stat.AP↗

arXiv2014

Experiences in Bayesian Inference in Baltic Salmon Management

We review a success story regarding Bayesian inference in fisheries management in the Baltic Sea. The management of salmon fisheries is currently based on the results of a complex Bayesian population dynamic model, and managers and stakeholders use the probabilities in their discussions. We also discuss the technical and human challenges in using Bayesian modeling to give practical advice to the public and to government officials and suggest future areas in which it can be applied. In particular, large databases in fisheries science offer flexible ways to use hierarchical models to learn the population dynamics parameters for those by-catch species that do not have similar large stock-specific data sets like those that exist for many target species. This information is required if we are to understand the future ecosystem risks of fisheries.

stat.ME↗