Compositional ideas in the bayesian analysis of categorical data with application to dose finding clinical trials

Compositional random vectors are fundamental tools in the Bayesian analysis of categorical data. Many of the issues that are discussed with reference to the statistical analysis of compositional data have a natural counterpart in the construction of a Bayesian statistical model for categorical data. This note builds on the idea of cross-fertilization of the two areas recommended by Aitchison (1986) in his seminal book on compositional data. Particular emphasis is put on the problem of what parameterization to use

The chemical variability at the surface of Mars: implications for exogenic processes

The chemical composition of sediments and rocks, as well as their distribution at the Martian surface, represent a long term archive of processes, which have formed the planetary surface. A survey of chemical compositions by means of Compositional Data Analysis represents a valuable tool to extract direct evidence for weathering processes and allows to quantify weathering and sedimentation rates. clr-biplot techniques are applied for visualization of chemical relationships across the surface (“chemical maps”). The variability among individual suites of data is further analyzed by means of clr-PCA, in order to extract chemical alteration vectors between fresh rocks and their crusts and for an assessment of different source reservoirs accessible to soil formation. Both techniques are applied to elucidate the influence of remote weathering by combined analysis of several soil forming branches. Vector analysis in the Simplex provides the opportunity to study atmosphere surface interactions, including the role and composition of volcanic gases

Using self organizing maps on compositional data

Self-organizing maps (Kohonen 1997) is a type of artificial neural network developed to explore patterns in high-dimensional multivariate data. The conventional version of the algorithm involves the use of Euclidean metric in the process of adaptation of the model vectors, thus rendering in theory a whole methodology incompatible with non-Euclidean geometries. In this contribution we explore the two main aspects of the problem: 1. Whether the conventional approach using Euclidean metric can shed valid results with compositional data. 2. If a modification of the conventional approach replacing vectorial sum and scalar multiplication by the canonical operators in the simplex (i.e. perturbation and powering) can converge to an adequate solution. Preliminary tests showed that both methodologies can be used on compositional data. However, the modified version of the algorithm performs poorer than the conventional version, in particular, when the data is pathological. Moreover, the conventional ap- proach converges faster to a solution, when data is \well-behaved". Key words: Self Organizing Map; Artificial Neural networks; Compositional data

Àlgebra vectorial: aplicacions lineals

Oferim als estudiants universitaris i als lectors interessats aquesta guia didàctica de la matemàtica universitària com a fruit dels nostres anys de docència de les matemàtiques a la Universitat. El resultat final ha esdevingut una col·lecció de setze petits volums agrupats en els dos mòduls d'Àlgebra Lineal i de Càlcul Infinitesimal. En aquest volum es generalitza en primer lloc el concepte d'aplicació entre dos espais vectorials i s'introdueix la important definició d'aplicació lineal. Pel seu estudi s'utilitza l'àlgebra matricial. A continuació es desenvolupen els temes de valors i vectors propis, la diagonalització d'endomorfismes i l'estudi de les formes quadràtiques

Scene Classification Using a Hybrid Generative/Discriminative Approach

We investigate whether dimensionality reduction using a latent generative model is beneficial for the task of weakly supervised scene classification. In detail, we are given a set of labeled images of scenes (for example, coast, forest, city, river, etc.), and our objective is to classify a new image into one of these categories. Our approach consists of first discovering latent ";topics"; using probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature here applied to a bag of visual words representation for each image, and subsequently, training a multiway classifier on the topic distribution vector for each image. We compare this approach to that of representing each image by a bag of visual words vector directly and training a multiway classifier on these vectors. To this end, we introduce a novel vocabulary using dense color SIFT descriptors and then investigate the classification performance under changes in the size of the visual vocabulary, the number of latent topics learned, and the type of discriminative classifier used (k-nearest neighbor or SVM). We achieve superior classification performance to recent publications that have used a bag of visual word representation, in all cases, using the authors' own data sets and testing protocols. We also investigate the gain in adding spatial information. We show applications to image retrieval with relevance feedback and to scene classification in videos

Introduction to Matlab - a self-study tutorial

Matlab is a high level language that is very easy to use and very powerful. It comes with a wealth of libraries and toolboxes, that you can use directly, so that you don't need to program low level functions. It enables you to display results very easily on graphs and images. To get started with it, you need to understand how to manipulate and represent data, and how to find information about the available functions. During this self-study tutorial, you will learn: 1- How to start Matlab. 2- How you can find out all the information you need. 3- How to create simple vectors and matrices. 4- What functions are available and how to find them. 5- How to plot graphs of functions. 6- How to write a script. After this (should take about an hour), you will know most of what you need to know about Matlab and should definitely know how to go on learning about it on your own…

Lecture notes D pdf

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MA158exam01

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Exam 1979 pdf

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MA158exam99pdf

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MA17xexam01

Exam questions and solutions in LaTex. Diagrams for the questions are all together in the support.zip file, as .eps files