Marginal likelihood computation for model selection and hypothesis testing: an extensive review

This is an up-to-date introduction to, and overview of, marginal likelihood computation for model selection and hypothesis testing. Computing normalizing constants of probability models (or ratio of constants) is a fundamental issue in many applications in statistics, applied mathematics, signal processing and machine learning. This article provides a comprehensive study of the state-of-the-art of the topic. We highlight limitations, benefits, connections and differences among the different techniques. Problems and possible solutions with the use of improper priors are also described. Some of the most relevant methodologies are compared through theoretical comparisons and numerical experiments.Comment: Keywords: Marginal likelihood, Bayesian evidence, numerical integration, model selection, hypothesis testing, quadrature rules, double-intractable posteriors, partition function

Integration of land use and transportation : selection of an appropriate model with Nashville MPO case study

This study analyzes the information concerning the process of a selection technique for integrated land use/transportation models. The research presents a general history of the integration of the modeling process. It also includes an inventory that describes land use, transportation, and other integrated models. The main focus of this analysis deals with the process of the selection of an appropriate model applied to a specific region or agency. The selection process includes an investigation of what constitutes a good integrated model and the actual selection process itself. This selection process is conceptualized as a matrix table illustrating the method, which allows models to be weighed according to appropriate criteria. The thesis begins with a brief history of the land use/ transportation modeling techniques. Following the history is an overview of some of the current and operational models for the integration process. The main focus of the research follows which entails the selection process of an appropriate model for a specific region. The study answers the question as to what is the best method for selecting a model. The research concludes with an analysis of the selection process for the Nashville Metropolitan Organization case study and their applications. The study reveals that each agency has to select the appropriate model to best fit their needs. To achieve this, an understanding must first set out to determine what the agency is trying to achieve. This study provides a planning agency or firm with the necessary information to achieve the right selection for their specific requirements

Accident analysis models and methods: guidance for safety professionals

Accident analysis models and methods provide safety professionals with a means of understanding why accidents occur. Choosing an analysis technique is, however, not a simple process. A wide range of methods are available; each offering various theoretical and practical benefits and drawbacks. Furthermore, individuals engaged in accident investigation are subjected to various factors, e.g. budgetary and time constraints, which can influence their selection and usage of an analysis tool. This report is based on an extensive review of the accident analysis literature and an interview study conducted with 42 safety experts and has two aims. Firstly, it provides an overview of the available analysis techniques and the factors influencing an individual’s choice and usage of these methods. The intention is to provide the reader with information that may enable them to make a more informed selection of analysis tool. The second aim is to present an analysis model currently used in industry. The intention is to provide the reader with a validated method that can be readily employed, if undertaking a detailed assessment of the available techniques is not practicable

Effects of Influential Points and Sample Size on the Selection and Replicability of Multivariable Fractional Polynomial Models

The multivariable fractional polynomial (MFP) procedure combines variable selection with a function selection procedure (FSP). For continuous variables, a closed test procedure is used to decide between no effect, linear, FP1 or FP2 functions. Influential observations (IPs) and small sample size can both have an impact on a selected fractional polynomial model. In this paper, we used simulated data with six continuous and four categorical predictors to illustrate approaches which can help to identify IPs with an influence on function selection and the MFP model. Approaches use leave-one or two-out and two related techniques for a multivariable assessment. In seven subsamples we also investigated the effects of sample size and model replicability. For better illustration, a structured profile was used to provide an overview of all analyses conducted. The results showed that one or more IPs can drive the functions and models selected. In addition, with a small sample size, MFP might not be able to detect non-linear functions and the selected model might differ substantially from the true underlying model. However, if the sample size is sufficient and regression diagnostics are carefully conducted, MFP can be a suitable approach to select variables and functional forms for continuous variables.Comment: Main paper and a supplementary combine

An application to select collaborative project management software tools

"The 2014 World Conference on Information Systems and Technologies (WorldCIST'14)"In an increasingly competitive market the use of project management techniques can help controlling scope, time, and cost in an efficient way. Either due to size or complexity that may exist in a project, it may be essential to use project management software tools. Some projects involve teams of people who may be geographically dispersed, being essential to exchange information among project stakeholders, hence the need for collaborative tools, best known as groupware. In this paper, we present an overview of project management and collaborative project management techniques and tools. Next, we present a framework, based on ISO 9126 and ISO 14598, to classify collaborative project management software tools. Finally, we present a model and an application to help on the selection of this type of tools.info:eu-repo/semantics/publishedVersio

Deep Learning for Scene Recognition from Visual Data:A Survey

The use of deep learning techniques has exploded during the last few years, resulting in a direct contribution to the field of artificial intelligence. This work aims to be a review of the state-of-the-art in scene recognition with deep learning models from visual data. Scene recognition is still an emerging field in computer vision, which has been addressed from a single image and dynamic image perspective. We first give an overview of available datasets for image and video scene recognition. Later, we describe ensemble techniques introduced by research papers in the field. Finally, we give some remarks on our findings and discuss what we consider challenges in the field and future lines of research. This paper aims to be a future guide for model selection for the task of scene recognition

Prospects for Exotica Searches at ATLAS and CMS Experiments

This paper presents an overview of prospects for searches for exotic physics beyond the Standard Model with the Large Hadron Collider at CERN. The results presented here are based on Monte Carlo simulations of the ATLAS and CMS detectors, assuming 100~pb$^{-1}$ of collected integrated luminosity and proton-proton collisions at $\sqrt{s} = 14$~TeV. A selection of benchmark analyses is discussed, including searches for new physics in the di-lepton, di-jet, and lepton-jet channel, and a description of techniques to identify the production of heavy long-lived charged particles. The impact on discovery potential of ATLAS and CMS of having collisions at an energy lower than the design of the machine is discussed