Problems at the intersection of compression and statistical inference recur frequently due to the concurrent use of signal and image compression and classification algorithms in many applications. This dissertation addresses two such problems: statistical inference on compressed data, and rate-allocation for joint compression and classification.Features of the JPEG2000 standard make possible the development of computationally efficient algorithms to achieve such a goal for imagery compressed using this standard. We propose the use of the information content (IC) of wavelet subbands, defined as the number of bytes that the JPEG2000 encoder spends to compress the subbands, for content analysis. Applying statistical learning frameworks for detection and classification, we present experimental results for compressed-domain texture image classification and cut detection in video. Our results indicate that reasonable performance can be achieved, while saving computational and bandwidth resources. IC features can also be used for preliminary analysis in the compressed domain to identify candidates for further analysis in the decompressed domain.In many applications of image compression, the compressed image is to be presented to human observers and statistical decision-making systems. In such applications, the fidelity criterion with respect to which the image is compressed must be selected to strike an appropriate compromise between the (possibly conflicting) image quality criteria for the human and machine observers. We present tractable distortion measures based on the Bhattacharyya distance (BD) and a new upper bound on the quantized probability of error that make possible closed form expressions for rate allocation to image subbands and show their efficacy in maintaining the aforementioned balance between compression and classification. The new bound offers two advantages over the BD in that it yields closed-form solutions for rate-allocation in problems involving correlated sources and more than two classes.

This study examines how students without formal statistics draw conclusions based on statistical data. The study refers to how pupil draw conclusions based on existing data, reason within the subject statistics and which aspects pupil may have difficulty in perceiving. To investigate this, the following research question were used: How do Swedish students without formal statistical training express informal statistical inferences?     This study is based on informal statistical inference which means that how to draw conclusions based on already existing data about what will happen in the next step. To collect data for this study, the data collection method focusgroup interviews with students in year 5 was used. The collected data were compiled, analyzed and compared with previous research. The results showed that students have difficulty drawing conclusions based on the existing data because they do not see it in a larger context.

Modern radar systems must operate in a wide variety of time-varying conditions. These include various types of interference from neighboring systems, self-interference or clutter, and targets with fluctuating responses. It has been well-established that the quality and nature of radar measurements depend heavily on the choice of signal transmitted by the radar. In this dissertation, we discuss techniques which may be used to adapt the radar's waveform on-the-fly while making very few a priori assumptions about the physical environment. By employing tools from reinforcement learning and online learning, we present a variety of algorithms which handle practical issues of the waveform selection problem that have been left open by previous works. In general, we focus on two key challenges inherent to the waveform selection problem, sample-efficiency and universality. Sample-efficiency corresponds to the number of experiences a learning algorithm requires to achieve desirable performance. Universality refers to the learning algorithm's ability to achieve desirable performance across a wide range of physical environments. Specifically, we develop a contextual bandit-based approach to vastly improve the sample-efficiency of learning compared to previous works. We then improve the generalization performance of this model by developing a Bayesian meta-learning technique. To handle the problem of universality, we develop a learning algorithm which is asymptotically optimal in any Markov environment having finite memory length. Finally, we compare the performance of learning-based waveform selection to fixed rule-based waveform selection strategies for the scenarios of dynamic spectrum access and multiple-target tracking. We draw conclusions as to when learning-based approaches are expected to significantly outperform rule-based strategies, as well as the converse. / Doctor of Philosophy / Modern radar systems must operate in a wide variety of time-varying conditions. These include various types of interference from neighboring systems, self-interference or clutter, and targets with fluctuating responses. It has been well-established that the quality and nature of radar measurements depend heavily on the choice of signal transmitted by the radar. In this dissertation, we discuss techniques which may be used to adapt the radar's waveform on-the-fly while making very few explicit assumptions about the physical environment. By employing tools from reinforcement learning and online learning, we present a variety of algorithms which handle practical and theoretical issues of the waveform selection problem that have been left open by previous works. We begin by asking the questions "What is cognitive radar?" and "When should cognitive radar be used?" in order to develop a broad mathematical framework for the signal selection problem. The latter chapters then deal with the role of intelligent real-time decision-making algorithms which select favorable signals for target tracking and interference mitigation. We conclude by discussing the possible roles of cognitive radar within future wireless networks and larger autonomous systems.

This diploma thesis provides an evaluation of Exponential families of distributions which has a special position in mathematical statistics. Diploma will learn the basic concepts and facts associated with the distribution of exponential type. Especially with focusing on the advantages of exponential families in classical parametric statistics, thus in theory of estimation and hypothesis testing. Emphasis will be placed on one-parameter and multi-parameters systems.

Title: Exponential families in statistical inference Author: Sally Abdel-Maksoud Department: Department of Probability and Mathematical Statistics Supervisor: doc. RNDr. Daniel Hlubinka, Ph.D. Supervisor's e-mail address: Daniel.Hlubinka@mff.cuni.cz Abstract: This diploma thesis provides an evaluation of Exponential families of distributions which has a special position in mathematical statistic including appropriate properties for estimation of population parameters, hypothesis testing and other inference problems. Diploma will introduce the basic concepts and facts associated with the distribution of exponential type especially with focusing on the advantages of exponential families in classical parametric statistics, thus in theory of estimation and hypothesis testing. Emphasis will be placed on one-parameter and multi- parameters systems. It also exposes an important concepts about the curvature of a statistical problem including the curvature in exponential families. We will define a quantity that measure how nearly "exponential" the families are. This quantity is said to be the statistical curvature of the family. We will show that the family with a small curvature enjoy the good properties of exponential families Moreover, the properties of the curvature, hypotheses testing and some...

<p>Dengue is an important vector-borne virus that infects on the order of 400 million individuals per year. Infection with one of the virus's four serotypes (denoted DENV-1 to 4) may be silent, result in symptomatic dengue 'breakbone' fever, or develop into the more severe dengue hemorrhagic fever/dengue shock syndrome (DHF/DSS). Extensive research has therefore focused on identifying factors that influence dengue infection outcomes. It has been well-documented through epidemiological studies that DHF is most likely to result from a secondary heterologous infection, and that individuals experiencing a DENV-2 or DENV-3 infection typically are more likely to present with more severe dengue disease than those individuals experiencing a DENV-1 or DENV-4 infection. However, a mechanistic understanding of how these risk factors affect disease outcomes, and further, how the virus's ability to evolve these mechanisms will affect disease severity patterns over time, is lacking. In the second chapter of my dissertation, I formulate mechanistic mathematical models of primary and secondary dengue infections that describe how the dengue virus interacts with the immune response and the results of this interaction on the risk of developing severe dengue disease. I show that only the innate immune response is needed to reproduce characteristic features of a primary infection whereas the adaptive immune response is needed to reproduce characteristic features of a secondary dengue infection. I then add to these models a quantitative measure of disease severity that assumes immunopathology, and analyze the effectiveness of virological indicators of disease severity. In the third chapter of my dissertation, I then statistically fit these mathematical models to viral load data of dengue patients to understand the mechanisms that drive variation in viral load. I specifically consider the roles that immune status, clinical disease manifestation, and serotype may play in explaining viral load variation observed across the patients. With this analysis, I show that there is statistical support for the theory of antibody dependent enhancement in the development of severe disease in secondary dengue infections and that there is statistical support for serotype-specific differences in viral infectivity rates, with infectivity rates of DENV-2 and DENV-3 exceeding those of DENV-1. In the fourth chapter of my dissertation, I integrate these within-host models with a vector-borne epidemiological model to understand the potential for virulence evolution in dengue. Critically, I show that dengue is expected to evolve towards intermediate virulence, and that the optimal virulence of the virus depends strongly on the number of serotypes that co-circulate. Together, these dissertation chapters show that dengue viral load dynamics provide insight into the within-host mechanisms driving differences in dengue disease patterns and that these mechanisms have important implications for dengue virulence evolution.</p> / Dissertation

In order for computers to interact with and understand the visual world, they must be equipped with reasoning systems that include high–level quantities such as objects, actions, and scenes. This thesis is concerned with extracting such representations of the world from visual input. The first part of this thesis describes an approach to scene understanding in which texture characteristics of the visual world are used to infer scene categories. We show that in the context of a moving camera, it is common to observe images containing very few individually salient image regions, yet overall texture structure often allows our system to derive powerful contextual cues about the environment. Our approach builds on ideas from texture recognition, and we show that our algorithm out–performs the well–known Gist descriptor on several classification tasks. In the second part of this thesis we we are interested in scene understanding in the context of multiple calibrated views of a scene, as might be obtained from a Structure–from–Motion or Simultaneous Localization and Mapping (SLAM) system. Though such systems are capable of localizing the camera robustly and efficiently, the maps produced are typically sparse point-clouds that are difficult to interpret and of little use for higher–level reasoning tasks such as scene understanding or human-machine interaction. In this thesis we begin to address this deficiency, presenting progress towards modeling scenes using semantically meaningful primitives such as floor, wall, and ceiling planes. To this end we adopt the indoor Manhattan representation, which was recently proposed for single–view reconstruction. This thesis presents the first in–depth description and analysis of this model in the literature. We describe a probabilistic model relating photometric features, stereo photo–consistencies, and 3D point clouds to Manhattan scene structure in a Bayesian framework. We then present a fast dynamic programming algorithm that solves exact MAP inference in this model in time linear in image size. We show detailed comparisons with the state–of–the art in both the single– and multiple–view contexts. Finally, we present a framework for learning within the indoor Manhattan hypothesis class. Our system is capable of extrapolating from labelled training examples to predict scene structure for unseen images. We cast learning as a structured prediction problem and show how to optimize with respect to two realistic loss functions. We present experiments in which we learn to recover scene structure from both single and multiple views — from the perspective of our learning algorithm these problems differ only by a change of feature space. This work constitutes one of the most complicated output spaces (in terms of internal constraints) yet considered within a structure prediction framework.

Energy efficiency measures in buildings and alternative heating methods have led to a decreased demand for district heating (DH). Furthermore, due to a recent increase in extreme weather events, it is harder for DH providers to maintain a steady production leading to increased costs. These issues have led DH companies to change their price models. This thesis investigated such a price model change, made by Mälarenergi (ME) on the 1st of August 2018. The aim was to compare the old price model (PM1) with the new price model (PM2) by investigating the choice of base and peak loads a customer can make for the upcoming year, and/or if they should let ME choose for them. A prediction method, based on predicting the hourly DH demand, was chosen after a literature study and several method comparisons were made from using weather parameters as independent variables. Consumption data from Mälarenergi for nine customers of different sizes were gathered, and eight weather parameters from 2014 to 2018 were implemented to build up the prediction model. The method comparison results from Unscrambler showed that multilinear regression was the most accurate statistical modelling method, which was later used for all predictions. These predictions from Unscrambler were then used in MATLAB to estimate the total annual cost for each customer and outcome. For PM1, the results showed that the flexible cost for the nine customers stands for 76 to 85 % of the total cost, with the remaining cost as fixed fees. For PM2, the flexible cost for the nine customers stands for 46 to 61 % of the total cost, with the remaining as fixed cost. Regarding the total cost, PM2 is on average 7.5 % cheaper than PM1 for smaller customer, 8.6 % cheaper for medium customers and 15.9 % cheaper for larger customers. By finding the lowest cost case for each customer their optimal base and peaks loads were found and with the use of a statistical inference method (Bootstrapping) a 95 % confidence interval for the base load and the total yearly cost with could be established. The conclusion regarding choices is that the customer should always choose their own base load within the recommended confidence interval, with ME’s choice seen as a recommendation. Moreover, ME should always make the peak load choice because they are willing to pay for an excess fee that the customer themselves must pay otherwise.

Evolution is an ubiquitous feature of living systems. The genetic composition of a population changes in response to the primary evolutionary forces: mutation, selection and genetic drift. Organisms undergoing rapid adaptation acquire multiple mutations that are physically linked in the genome, so their fates are mutually dependent and selection only acts on these loci in their entirety. This aspect has been largely overlooked in the study of asexual or somatic evolution and plays a major role in the evolution of bacterial and viral infections and cancer. In this thesis, we put forward a theoretical description for a minimal model of evolutionary dynamics to identify driver mutations, which carry a large positive fitness effect, among passenger mutations that hitchhike on successful genomes. We examine the effect this mode of selection has on genomic patterns of variation to infer the location of driver mutations and estimate their selection coefficient from time series of mutation frequencies. We then present a probabilistic model to reconstruct genotypically distinct lineages in mixed cell populations from DNA sequencing. This method uses Hidden Markov Models for the deconvolution of genetically diverse populations and can be applied to clonal admixtures of genomes in any asexual population, from evolving pathogens to the somatic evolution of cancer. To understand the effects of selection on rapidly adapting populations, we constructed sequence ensembles in a recombinant library of budding yeast (S. cerevisiae). Using DNA sequencing, we characterised the directed evolution of these populations under selective inhibition of rate-limiting steps of the cell cycle. We observed recurrent patterns of adaptive mutations and characterised common mutational processes, but the spectrum of mutations at the molecular level remained stochastic. Finally, we investigated the effect of genetic variation on the fate of new mutations, which gives rise to complex evolutionary dynamics. We demonstrate that the fitness variance of the population can set a selective threshold on new mutations, setting a limit to the efficiency of selection. In summary, we combined statistical analyses of genomic sequences, mathematical models of evolutionary dynamics and experiments in molecular evolution to advance our understanding of rapid adaptation. Our results open new avenues in our understanding of population dynamics that can be translated to a range of biological systems.

The past two decades have shown an unexpected effectiveness of Web-scale data in natural language processing. Even the simplest models, when paired with unprecedented amounts of unstructured and unlabeled Web data, have been shown to outperform sophisticated ones. It has been argued that the effectiveness of Web-scale data has undermined the necessity of sophisticated modeling or laborious data set curation. In this thesis, we argue for and illustrate an alternative view, that Web-scale data not only serves to improve the performance of simple models, but also can allow the use of qualitatively more sophisticated models that would not be deployable otherwise, leading to even further performance gains. / Engineering and Applied Sciences