Search results for "hierarchical data."
showing 7 items of 17 documents
Towards a More Effective Hospital: Helping Health Professionals to Learn from their Own Practice by Developing an Easy to use Clinical Processes Quer…
2016
Application of complex socio-technical systems theory to optimization of clinical processes in hospitals highlights the importance of the acceptance and promotion of responsible autonomy among health professionals. Therefore the independent ability for clinicians to search for answers to questions which are outside the scope of pre-made reports is important. However, the ad-hoc data querying process is slow and error prone due to inability of health professionals to access data directly without involving IT experts. The problem lies in the complexity of means used to query data. We propose a new natural language- and star ontology-based ad-hoc data querying approach which reduces the steep …
Bayesian hierarchical models in manufacturing bulk service queues
2006
In this paper, Queueing Theory and Bayesian statistical tools are used to analyze the congestion of various manufacturing bulk service queues with the same characteristics that are working independently of one another and in equilibrium. Hierarchical models are discussed in order to develop the whole inferential process for the parameters governing the system. Markov Chain Monte Carlo methods and numerical inversion of transforms are addressed to compute the posterior predictive distributions of the usual measures of performance in practice.
Probabilistic small area risk assessment using GIS-based data: a case study on Finnish childhood diabetes
2000
A Bayesian hierarchical spatial model is constructed to describe the regional incidence of insulin dependent diabetes mellitus (IDDM) among the under 15-year-olds in Finland. The model exploits aggregated pixel-wise locations for both the cases and the population at risk. Typically such data arise from combining geographic information systems (GIS) with large databases. The dates of diagnosis and locations of the cases are observed from 1987 to 1996. The population at risk counts are available for every second year during the same period. A hierarchical model is suggested for the pixel wise case counts, including a population model to account for the uncertainty of the population at risk ov…
Bayesian Design of “Successful” Replications
2002
Replication of experiments is commonin applied research. However, systematic studies of the goals and motivations of a “replication” are rare. As a consequence, there does not seem to be a precise notion of what a “success” when replicating means. This article discusses some of the possible goals for replication; this leads to different (but precise) notions of “success” when replicating. Bayesian hierarchical models allow for a flexible and explicit incorporation of the assumed relationship among the experiments. Bayesian predictive distributions are a natural tool to compute the probability of the replication being successful, and hence to design the replication so that the probability of…
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…
A hierarchical model for novel schemes of electrodialysis desalination
2019
Abstract A new hierarchical model for the electrodialysis (ED) process is presented. The model has been implemented into gPROMs Modelbuilder (PSE), allowing the development of a distributed-parameters simulation tool that combines the effectiveness of a semi-empirical modelling approach to the flexibility of a layered arrangement of modelling scales. Thanks to its structure, the tool makes possible the simulation of many different and complex layouts, requiring only membrane properties as input parameters (e.g. membrane resistance or salt and water permeability). The model has been validated against original experimental data obtained from a lab scale ED test rig. Simulation results concern…
Multilevel Latent Profile Analysis With Covariates : Identifying Job Characteristics Profiles in Hierarchical Data as an Example
2018
Latent profile analysis (LPA) is a person-centered method commonly used in organizational research to identify homogeneous subpopulations of employees within a heterogeneous population. However, in the case of nested data structures, such as employees nested in work departments, multilevel techniques are needed. Multilevel LPA (MLPA) enables adequate modeling of subpopulations in hierarchical data sets. MLPA enables investigation of variability in the proportions of Level 1 profiles across Level 2 units, and of Level 2 latent classes based on the proportions of Level 1 latent profiles and Level 1 ratings, and the extent to which covariates drawn from the different hierarchical levels of th…