Search results for " MCMC."

Data Augmentation Approach in Bayesian Modelling of Presence-only Data

2011

Abstract Ecologists are interested in prediction of potential distribution of species in suitable areas, essential for planning conservation and management strategies. Unfortunately, often the only available information in such studies is the true presence of the species at few locations of the study area and the associated environmental covariates over the entire area, referred as presence-only data. We propose a Bayesian approach to estimate logistic linear regressions adapted to presence-only data through the introduction of a random approximation of the correction factor in the adjusted logistic model that allows us to overcome the need to know a priori the prevalence of the species.

Comparison of different uncertainty techniques in urban stormwater quantity and quality modelling

2011

Abstract Urban drainage models are important tools used by both practitioners and scientists in the field of stormwater management. These models are often conceptual and usually require calibration using local datasets. The quantification of the uncertainty associated with the models is a must, although it is rarely practiced. The International Working Group on Data and Models, which works under the IWA/IAHR Joint Committee on Urban Drainage, has been working on the development of a framework for defining and assessing uncertainties in the field of urban drainage modelling. A part of that work is the assessment and comparison of different techniques generally used in the uncertainty assessm…

Bayesian inference for the extremal dependence

2016

A simple approach for modeling multivariate extremes is to consider the vector of component-wise maxima and their max-stable distributions. The extremal dependence can be inferred by estimating the angular measure or, alternatively, the Pickands dependence function. We propose a nonparametric Bayesian model that allows, in the bivariate case, the simultaneous estimation of both functional representations through the use of polynomials in the Bernstein form. The constraints required to provide a valid extremal dependence are addressed in a straightforward manner, by placing a prior on the coefficients of the Bernstein polynomials which gives probability one to the set of valid functions. The…

Spatial Bayesian Modeling of Presence-only Data

2011

Particle Group Metropolis Methods for Tracking the Leaf Area Index

2020

Monte Carlo (MC) algorithms are widely used for Bayesian inference in statistics, signal processing, and machine learning. In this work, we introduce an Markov Chain Monte Carlo (MCMC) technique driven by a particle filter. The resulting scheme is a generalization of the so-called Particle Metropolis-Hastings (PMH) method, where a suitable Markov chain of sets of weighted samples is generated. We also introduce a marginal version for the goal of jointly inferring dynamic and static variables. The proposed algorithms outperform the corresponding standard PMH schemes, as shown by numerical experiments.

Bayesian Analysis of Diagnostic Accuracy for Gastroesophageal Reflux Disease in the absence of gold standard

2007

Valkosolupitoisuuksien bayesilainen mallintaminen lasten leukemian ylläpitohoidossa

2018

Lasten akuutin lymfoblastileukemian ylläpitovaiheen hoidossa tehtävät lääkeannostuspäätökset pohjataan nykyisin potilaan veren valkosolupitoisuuteen, joka on hoidon tehokkuudesta kertova tekijä. Potilaalle sopiva lääkeannostus on hoidon onnistumisen ja turvallisuuden kannalta tärkeä, mutta sen löytäminen on vaikeaa, sillä annettu lääkitys näkyy valkosolupitoisuudessa viiveellä, ja potilaiden elimistön reagointi lääkitykseen on yksilöllistä. Sopivan lääkeannostuksen löytämistä hankaloittavat myös hoidonaikaiset tulehdukset, jotka voivat muuttaa valkosolupitoisuutta hetkellisesti. Työ käsittelee akuuttiin lymfoblastileukemiaan sairastuneiden suomalaisten potilaiden veren valkosolupitoisuuden …