Search results for "bayesian hierarchical model"
showing 10 items of 15 documents
Incorporating Biotic Information in Species Distribution Models: A Coregionalized Approach
2021
In this work, we discuss the use of a methodological approach for modelling spatial relationships among species by means of a Bayesian spatial coregionalized model. Inference and prediction is performed using the integrated nested Laplace approximation methodology to reduce the computational burden. We illustrate the performance of the coregionalized model in species interaction scenarios using both simulated and real data. The simulation demonstrates the better predictive performance of the coregionalized model with respect to the univariate models. The case study focus on the spatial distribution of a prey species, the European anchovy (Engraulis encrasicolus), and one of its predator spe…
Bayesian spatio-temporal approach to identifying fish nurseries by validating persistence areas
2015
Spatial and temporal closures of fish nursery areas to fishing have recently been recognized as useful tools for efficient fisheries management, as they preserve the reproductive potential of populations and increase the recruitment of target species. In order to identify and locate potential nursery areas for spatio-temporal closures, a solid understanding of species− environment relationships is needed, as well as spatial identification of fish nurseries through the application of robust analyses. One way to achieve knowledge of fish nurseries is to analyse the persistence of recruitment hotspots. In this study, we propose the comparison of different spatiotemporal model structures to ass…
Fishery-dependent and -independent data lead to consistent estimations of essential habitats
2016
AbstractSpecies mapping is an essential tool for conservation programmes as it provides clear pictures of the distribution of marine resources. However, in fishery ecology, the amount of objective scientific information is limited and data may not always be directly comparable. Information about the distribution of marine species can be derived from two main sources: fishery-independent data (scientific surveys at sea) and fishery-dependent data (collection and sampling by observers in commercial vessels). The aim of this paper is to compare whether these two different sources produce similar, complementary, or different results. We compare them in the specific context of identifying the Es…
VARIABLE SELECTION FOR NOISY DATA APPLIED IN PROTEOMICS
2014
International audience; The paper proposes a variable selection method for pro-teomics. It aims at selecting, among a set of proteins, those (named biomarkers) which enable to discriminate between two groups of individuals (healthy and pathological). To this end, data is available for a cohort of individuals: the biological state and a measurement of concentrations for a list of proteins. The proposed approach is based on a Bayesian hierarchical model for the dependencies between biological and instrumental variables. The optimal selection function minimizes the Bayesian risk, that is to say the selected set of variables maximizes the posterior probability. The two main contributions are: (…
Spatio temporal modeling of species distribution
2019
The aim of this thesis is study spatial distribution of different groups from different perspectives and to analyse the different approaches to this problem. We move away from the classical approach, commonly used by ecologists, to more complex solutions, already applied in several disciplines. We are focused in applying advanced modelling techniques in order to understand species distribution and species behaviour and the relationships between them and environmental factors and have used first the most common models applied in ecology to move then to more advanced and complex perspectives. From a general perspective and comparing the different models applied during the process, from MaxEnt…
Bayesian Methodology in Statistics
2009
Bayesian methods provide a complete paradigm for statistical inference under uncertainty. These may be derived from an axiomatic system and provide a coherent methodology which makes it possible to incorporate relevant initial information, and which solves many of the difficulties that frequentist methods are known to face. If no prior information is to be assumed, the more frequent situation met in scientific reporting, a formal initial prior function, the reference prior, mathematically derived from the assumed model, is used; this leads to objective Bayesian methods, objective in the precise sense that their results, like frequentist results, only depend on the assumed model and the data…
A Bayesian unified framework for risk estimation and cluster identification in small area health data analysis.
2020
Many statistical models have been proposed to analyse small area disease data with the aim of describing spatial variation in disease risk. In this paper, we propose a Bayesian hierarchical model that simultaneously allows for risk estimation and cluster identification. Our model formulation assumes that there is an unknown number of risk classes and small areas are assigned to a risk class by means of independent allocation variables. Therefore, areas within each cluster are assumed to share a common risk but they may be geographically separated. The posterior distribution of the parameter representing the number of risk classes is estimated using a novel procedure that combines its prior …
Prediction and Surveillance Sampling Assessment in Plant Nurseries and Fields
2022
In this paper, we propose a structured additive regression (STAR) model for modeling the occurrence of a disease in fields or nurseries. The methodological approach involves a Gaussian field (GF) affected by a spatial process represented by an approximation to a Gaussian Markov random field (GMRF). This modeling allows the building of maps with prediction probabilities regarding the presence of a disease in plants using Bayesian kriging. The advantage of this modeling is its computational benefit when compared with known spatial hierarchical models and with the Bayesian inference based on Markov chain Monte Carlo (MCMC) methods. Inference through the use of the integrated nested Laplace app…
A novel approach to quantifying the sensitivity of current and future cosmological datasets to the neutrino mass ordering through Bayesian hierarchic…
2017
We present a novel approach to derive constraints on neutrino masses from cosmological data, while taking into account our ignorance of the neutrino mass ordering. We derive constraints from a combination of current and future cosmological datasets on the total neutrino mass $M_\nu$ and on the mass fractions carried by each of the mass eigenstates, after marginalizing over the (unknown) neutrino mass ordering, either normal (NH) or inverted (IH). The bounds take therefore into account the uncertainty related to our ignorance of the mass hierarchy. This novel approach is carried out in the framework of Bayesian analysis of a typical hierarchical problem. In this context, the choice of the ne…
What Bayesians Expect of Each Other
1991
Abstract Our goal is to study general properties of one Bayesian's subjective beliefs about the behavior of another Bayesian's subjective beliefs. We consider two Bayesians, A and B, who have different subjective distributions for a parameter θ, and study Bayesian A's expectation of Bayesian B's posterior distribution for θ given some data Y. We show that when θ can take only two values, Bayesian A always expects Bayesian B's posterior distribution to lie between the prior distributions of A and B. Conditions are given under which a similar result holds for an arbitrary real-valued parameter θ. For a vector parameter θ we present useful expressions for the mean vector and covariance matrix …