Statistical models and inference for spatial point patterns with intensity-dependent marks
PublisherUniversity of Jyväskylä
Bayesian modelling Bitterlich sampling density-dependence Gaussian excursion set log Gaussian Cox process mark-dependent thinning marked point process MCMC pine samplings random set marked Cox process tropical rainforest tilastomenetelmät bayesilainen menetelmä Monte Carlo -menetelmät algoritmit sademetsät
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Chakraborty, Antik; Ovaskainen, Otso; Dunson, David B. (Institute of Mathematical Statistics, 2022)We introduce a new class of semiparametric latent variable models for long memory discretized event data. The proposed methodology is motivated by a study of bird vocalizations in the Amazon rain forest; the timings of ...
Kotamäki, Niina (University of Jyväskylä, 2018)Decision-making at different phases of adaptive river basin management planning rely largely on the information that is gained through environmental monitoring. The aim of this thesis was to develop and test statistical ...
Can visualization alleviate dichotomous thinking : Effects of visual representations on the cliff effect Helske, Jouni; Helske, Satu; Cooper, Matthew; Ynnerman, Anders; Besancon, Lonni (IEEE, 2021)Common reporting styles for statistical results in scientific articles, such as \pvalues\ and confidence intervals (CI), have been reported to be prone to dichotomous interpretations, especially with respect to the null ...
Vihola, Matti; Helske, Jouni; Franks, Jordan (Wiley-Blackwell, 2020)We consider importance sampling (IS) type weighted estimators based on Markov chain Monte Carlo (MCMC) targeting an approximate marginal of the target distribution. In the context of Bayesian latent variable models, the ...
On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction Vihola, Matti; Franks, Jordan (Oxford University Press, 2020)Approximate Bayesian computation enables inference for complicated probabilistic models with intractable likelihoods using model simulations. The Markov chain Monte Carlo implementation of approximate Bayesian computation ...