Global ETD Search

11	Basal Metabolic Rate (BMR) estimation using Probabilistic Graphical Models Jackson, Zara January 2019 (has links) Obesity is a growing problem globally. Currently 2.3 billion adults are overweight, and this number is rising. The most common method for weight loss is calorie counting, in which to lose weight a person should be in a calorie deficit. Basal Metabolic Rate accounts for the majority of calories a person burns in a day and it is therefore a major contributor to accurate calorie counting. This paper uses a Dynamic Bayesian Network to estimate Basal Metabolic Rate (BMR) for a sample of 219 individuals from all Body Mass Index (BMI) categories. The data was collected through the Lifesum app. A comparison of the estimated BMR values was made with the commonly used Harris Benedict equation, finding that food journaling is a sufficient method to estimate BMR. Next day weight prediction was also computed based on the estimated BMR. The results stated that the Harris Benedict equation produced more accurate predictions than the metabolic model proposed, therefore more work is necessary to find a model that accurately estimates BMR. Basal Metabolic Rate Resting Metabolic Rate Dynamic Bayesian Networks Temporal Models Food Tracking Calories Obesity Pymc3 Probabilistic Programming Probability Theory and Statistics Sannolikhetsteori och statistik
12	Evaluation of Probabilistic Programming Frameworks Munkby, Carl January 2022 (has links) In recent years significant progress has been made in the area of Probabilistic Programming, contributing to a considerably easier workflow for quantitative research in many fields. However, as new Probabilistic Programming Frameworks (PPFs) are continuously being created and developed, there is a need for finding ways of evaluating and benchmarking these frameworks. To this end, this thesis explored the use of a range of evaluation measures to evaluate and better understand the performance of three PPFs: Stan, NumPyro and TensorFlow Probability (TFP). Their respective Hamiltonian Monte Carlo (HMC) samplers were benchmarked on three different hierarchical models using both centered and non-centered parametrizations. The results showed that even if the same inference algorithms were used, the PPFs’ samplers still exhibited different behaviours, which consequently lead to non-negligible differences in their statistical efficiency. Furthermore, the sampling behaviour of the PPFs indicated that the observed differences can possibly be attributed to how the warm-up phase used in HMC-sampling is constructed. Finally, this study concludes that the computational speed of the numerical library used, was the primary deciding factor of performance in this benchmark. This was demonstrated by NumPyros superior computational speed, contributing to it yielding up to 10x higher ESSmin/s than Stan and 4x higher ESSmin/s than TFP. Computational statistics Bayesian statistics Probabilistic programming Probabilistic modelling Stan TensorFlow Probability Pyro NumPyro Markov Chain Monte Carlo Hamiltonian Monte Carlo NUTS Probability Theory and Statistics Sannolikhetsteori och statistik
13	Programming tools for intelligent systems Considine, Breandan 04 1900 (has links) Les outils de programmation sont des programmes informatiques qui aident les humains à programmer des ordinateurs. Les outils sont de toutes formes et tailles, par exemple les éditeurs, les compilateurs, les débogueurs et les profileurs. Chacun de ces outils facilite une tâche principale dans le flux de travail de programmation qui consomme des ressources cognitives lorsqu’il est effectué manuellement. Dans cette thèse, nous explorons plusieurs outils qui facilitent le processus de construction de systèmes intelligents et qui réduisent l’effort cognitif requis pour concevoir, développer, tester et déployer des systèmes logiciels intelligents. Tout d’abord, nous introduisons un environnement de développement intégré (EDI) pour la programmation d’applications Robot Operating System (ROS), appelé Hatchery (Chapter 2). Deuxièmement, nous décrivons Kotlin∇, un système de langage et de type pour la programmation différenciable, un paradigme émergent dans l’apprentissage automatique (Chapter 3). Troisièmement, nous proposons un nouvel algorithme pour tester automatiquement les programmes différenciables, en nous inspirant des techniques de tests contradictoires et métamorphiques (Chapter 4), et démontrons son efficacité empirique dans le cadre de la régression. Quatrièmement, nous explorons une infrastructure de conteneurs basée sur Docker, qui permet un déploiement reproductible des applications ROS sur la plateforme Duckietown (Chapter 5). Enfin, nous réfléchissons à l’état actuel des outils de programmation pour ces applications et spéculons à quoi pourrait ressembler la programmation de systèmes intelligents à l’avenir (Chapter 6). / Programming tools are computer programs which help humans program computers. Tools come in all shapes and forms, from editors and compilers to debuggers and profilers. Each of these tools facilitates a core task in the programming workflow which consumes cognitive resources when performed manually. In this thesis, we explore several tools that facilitate the process of building intelligent systems, and which reduce the cognitive effort required to design, develop, test and deploy intelligent software systems. First, we introduce an integrated development environment (IDE) for programming Robot Operating System (ROS) applications, called Hatchery (Chapter 2). Second, we describe Kotlin∇, a language and type system for differentiable programming, an emerging paradigm in machine learning (Chapter 3). Third, we propose a new algorithm for automatically testing differentiable programs, drawing inspiration from techniques in adversarial and metamorphic testing (Chapter 4), and demonstrate its empirical efficiency in the regression setting. Fourth, we explore a container infrastructure based on Docker, which enables reproducible deployment of ROS applications on the Duckietown platform (Chapter 5). Finally, we reflect on the current state of programming tools for these applications and speculate what intelligent systems programming might look like in the future (Chapter 6). Kotlin Docker Génie logiciel Software engineering Apprentissage automatique Machine learning Mathématiques appliquées Applied mathematics Langages de programmation Programming languages Outils de programmation Programming tools Systèmes de type Type systems Différenciation automatique Automatic differentiation Tests automatisés Automated testing Systémes embarqués Embedded systems Systèmes intelligents Intelligent systems Machines virtuelles Virtual machines ROS Metamorphic testing Programmation différenciable Differentiable programming Programmation probabiliste Probabilistic programming Programmation fonctionnelle Functional programming Compilateurs Compilers Generative modeling Modélisation générative Rétropropagation Backpropagation Fuzzing Test métamorphique Test de propriété Metamorphic testing Property-based testing Analyse statique Static analysis Moteur de production Build automation Intégration continue Continuous integration

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