Search results for "Hierarchical model"

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…

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…

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…

Hierarchies of Self-Organizing Maps for action recognition

2016

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 learns to represent action prototypes. The third - and last - layer of the hierarchy consists of a neural network that learns to label action prototypes of the second-laye…

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 …

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…

Learning Bayesian Metanetworks from Data with Multilevel Uncertainty

2006

Managing knowledge by maintaining it according to dynamic context is among the basic abilities of a knowledge-based system. The two main challenges in managing context in Bayesian networks are the introduction of contextual (in)dependence and Bayesian multinets. We are presenting one possible implementation of a context sensitive Bayesian multinet-the Bayesian Metanetwork, which implies that interoperability between component Bayesian networks (valid in different contexts) can be also modelled by another Bayesian network. The general concepts and two kinds of such Metanetwork models are considered. The main focus of this paper is learning procedure for Bayesian Metanetworks.

Natural induction: An objective bayesian approach

2009

The statistical analysis of a sample taken from a finite population is a classic problem for which no generally accepted objective Bayesian results seem to exist. Bayesian solutions to this problem may be very sensitive to the choice of the prior, and there is no consensus as to the appropriate prior to use.