Estimating aggregated nutrient fluxes in four Finnish rivers via Gaussian state space models

Abstract Reliable estimates of the nutrient fluxes carried by rivers from land-based sources to the sea are needed for efficient abatement of marine eutrophication. Although nutrient concentrations in rivers generally display large temporal variation, sampling and analysis for nutrients, unlike flow measurements, are rarely performed on a daily basis. The infrequent data calls for ways to reliably estimate the nutrient concentrations of the missing days. Here, we use the Gaussian state space models with daily water flow as a predictor variable to predict missing nutrient concentrations for four agriculturally impacted Finnish rivers.

Minimum description length based hidden Markov model clustering for life sequence analysis

Abstract In this article, a model-based method for clustering life sequences is suggested. In the social sciences, model-free clustering methods are often used in order to find typical life sequences. The suggested method, which is based on hidden Markov models, provides principled probabilistic ranking of candidate clusterings for choosing the best solution. After presenting the principle of the method and algorithm, the method is tested with real life data, where it finds eight descriptive clusters with clear probabilistic structures.