Aineiston tiedot

Tekijä (t):Valtonen, Kimmo
Mononen, Tommi
Myllymäki, Petri
Tirri, Henry
Erkinaro, Jaakko
Jokikokko, Erkki
Kuikka, Sakari
Romakkaniemi, Atso
Karlsson, Lars
Perä, Ingemar
Aineiston nimi:Cross-Analysis of Gulf of Bothnia Wild Salmon Rivers Using Bayesian Networks
Verkko-osoite:https://pdfs.semanticscholar.org/6b26/c1904830aea9a6319d078423c8cd9bd5a930.pdf
Aineistolaji:artikkeli
Asiasanat:Itämeri, kalantutkimus - kalatutkimus, lohi, virtavedet
Kustantaja:Helsinki institute for information technology
Painopaikka:Helsinki
Vuosi:2002
Sivumäärä:26
ISBN/ISSN:1458-9451
Lehti/sarja ym.:
Lisätietoa:Abstract: We present a methodology allowing the transfer of knowledge from a wild salmon river to another via a predictive model for the chosen population status indicator. From the management point of view, the production of wild smolts is the most important of such indicators. However, in our real-world data from Finnish and Swedish Gulf of Bothnia rivers we only have data on the number of wild smolts available for two of the rivers, making the direct empirical learning and validation of models learned from the data for the other rivers impossible, but the suggested methodology can be used to transfer knowledge from the two rivers to the other rivers. To validate the suggested approach, we also apply the methodology in the prediction of parr density, in which case the results can be validated, and check by strict empirical procedures for our success in the transfer of knowledge. Our framework is probabilistic and our approach Bayesian, allowing us to handle uncertainty in a consistent and well-defined fashion. Our model family is Bayesian networks, a class of models with a simple graphical representation allowing visualization of the obtained knowledge, being also the state-of-the-art classifier in many domains. Our emphasis is on empirical modeling: our aim is to see what can be learned from the existing real-world data. With the needs of fisheries management in mind, we highlight the role of the loss function in modeling, evaluating our models also in a setting where it is a greater error to o
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