Search results for "Hierarchical models"

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…

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…

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…

Outlier detection to hierarchical and mixed effects models

2008

Hierarchical and mixed effects models are models where a varying number of coefficients may be random at different levels of the hierarchy. The purpose of outlier analysis for these models is to determine whether an outlying unit at higher level is entirely outlying, or outlying due to effect of one or a few aberrant lower level units. Most works on diagnostics for these complex models have focused on the mixed model rather than on the hierarchical models, obscuring some relevant aspects of the hierarchical model. In this paper we will present an approach to influence analysis and outlier detection for mixed and hierarchical model, focusing on the special structure of nested data that these…

How do we understand other's intentions? - An implementation of mindreading in artificial systems -

Action Recognition based on Hierarchical Self-Organizing Maps

2014

We propose a hierarchical neural architecture able to recognise observed human actions. Each layer in the architecture represents increasingly complex human activity features. The first layer consists of a SOM which performs dimensionality reduction and clustering of the feature space. It represents the dynamics of the stream of posture frames in action sequences as activity trajectories over time. The second layer in the hierarchy consists of another SOM which clusters the activity trajectories of the first-layer SOM and thus it learns to represent action prototypes independent of how long the activity trajectories last. The third layer of the hierarchy consists of a neural network that le…

Analysis of Low-Altitude Aerial Sequences for Road Traffic Diagnosis using Graph Partitioning and Markov Hierarchical Models

2016

International audience; This article focuses on an original approach aiming the processing of low-altitude aerial sequences taken from an helicopter (or drone) and presenting a road traffic. Proposed system attempts to extract vehicles from acquired sequences. Our approach begins with detecting the primitives of sequence images. At the time of this step of segmentation, the system computes dominant motion for each pair of images. This motion is computed using wavelets analysis on optical flow equation and robust techniques. Interesting areas (areas not affected by the dominant motion) are detected thanks to a Markov hierarchical model. Primitives stemming from segmentation and interesting a…

Peer effects in the light of students interactions and the subjective dimensions of school experience

2011

This Thesis addresses the issue of peer-effects in the context of school. From analysis of a large database produced by a Chilean national study (SIMCE 2004), this work investigates the mechanisms through which pupils with different levels of scholastic, human and cultural capital influence each other. These influences seem present for a diverse range of school outcomes, including academic achievement. Drawing on the literature produced by different disciplinary approaches —sociology, economics, social psychology and education— the study focuses on ways of identifying and measuring peer-effects. The presence of subjective dimensions capable of reflecting, in part, the school experience of p…

Conditional predictive inference for online surveillance of spatial disease incidence

2011

This paper deals with the development of statistical methodology for timely detection of incident disease clusters in space and time. The increasing availability of data on both the time and the location of events enables the construction of multivariate surveillance techniques, which may enhance the ability to detect localized clusters of disease relative to the surveillance of the overall count of disease cases across the entire study region. We introduce the surveillance conditional predictive ordinate as a general Bayesian model-based surveillance technique that allows us to detect small areas of increased disease incidence when spatial data are available. To address the problem of mult…

Using the hierarchical modeling approach to derive spatial distribution of precipitation and temperature datasets. A case study for the area of Sicil…

2013

The interest for spatial interpolating climatic variables available by means of point measurements, as precipitation and temperature, arises from different needs, ranging from their usage for hydrological models to the reconstruction of climatic atlas of spatially distributed data. In some areas the spatial distribution of these variables can be related to the extremely variable morphology of the area. While simple deterministic interpolation methods usually produce just the spatial distribution of the variable of interest, implicitly relying on the spatial autocorrelation and manually tuning a few parameters, more complex statistical models, are able to derive the uncertainty associated wi…