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Bayesian Methods for Statistical Analysis

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ISBN: 9781921934254 Year: DOI: 10.26530/OAPEN_611011 Language: English
Publisher: ANU Press
Subject: Mathematics
Added to DOAB on : 2016-06-24 11:01:04
License: ANU Press

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Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide variety of data. The book consists of 12 chapters, starting with basic concepts and covering numerous topics, including Bayesian estimation, decision theory, prediction, hypothesis testing, hierarchical models, Markov chain Monte Carlo methods, finite population inference, biased sampling and nonignorable nonresponse. The book contains many exercises, all with worked solutions, including complete computer code. It is suitable for self-study or a semester-long course, with three hours of lectures and one tutorial per week for 13 weeks.

Quantitative Assessment and Validation of Network Inference Methods in Bioinformatics

Authors: ---
Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889194780 Year: Pages: 191 DOI: 10.3389/978-2-88919-478-0 Language: English
Publisher: Frontiers Media SA
Subject: Biotechnology --- General and Civil Engineering --- Genetics --- Science (General)
Added to DOAB on : 2016-03-10 08:14:33
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Scientists today have access to an unprecedented arsenal of high-tech tools that can be used to thoroughly characterize biological systems of interest. High-throughput “omics” technologies enable to generate enormous quantities of data at the DNA, RNA, epigenetic and proteomic levels. One of the major challenges of the post-genomic era is to extract functional information by integrating such heterogeneous high-throughput genomic data. This is not a trivial task as we are increasingly coming to understand that it is not individual genes, but rather biological pathways and networks that drive an organism’s response to environmental factors and the development of its particular phenotype. In order to fully understand the way in which these networks interact (or fail to do so) in specific states (disease for instance), we must learn both, the structure of the underlying networks and the rules that govern their behavior. In recent years there has been an increasing interest in methods that aim to infer biological networks. These methods enable the opportunity for better understanding the interactions between genomic features and the overall structure and behavior of the underlying networks. So far, such network models have been mainly used to identify and validate new interactions between genes of interest. But ultimately, one could use these networks to predict large-scale effects of perturbations, such as treatment by multiple targeted drugs. However, currently, we are still at an early stage of comprehending methods and approaches providing a robust statistical framework to quantitatively assess the quality of network inference and its predictive potential. The scope of this Research Topic in Bioinformatics and Computational Biology aims at addressing these issues by investigating the various, complementary approaches to quantify the quality of network models. These “validation” techniques could focus on assessing quality of specific interactions, global and local structures, and predictive ability of network models. These methods could rely exclusively on in silico evaluation procedures or they could be coupled with novel experimental designs to generate the biological data necessary to properly validate inferred networks.

Challenges to Mean-Based Analysis in Psychology: The Contrast Between Individual People and General Science

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889450435 Year: Pages: 114 DOI: 10.3389/978-2-88945-043-5 Language: English
Publisher: Frontiers Media SA
Subject: Science (General) --- Psychology
Added to DOAB on : 2018-02-27 16:16:44
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In a recent paper we (Speelman & McGann, 2013) argued that psychology’s reliance on data analysis methods that are based on group averages has resulted in a science of group phenomena that may be misleading about the nature of and reasons for individual behaviour. The paper highlighted a tension between a science in search of general laws on the one hand, and the individual, variable, and diverse nature of human behaviour on the other. This Research Topic explored this concern about the pitfalls of using the mean for the basis of psychological science. The problem is universal in its applicability to psychology, and opinion papers, reviews, and original empirical research from all areas of the discipline were invited. A total of 16 authors contributed 9 articles to the Topic. The range of issues that the authors viewed through the lens provided is impressive. The papers in this collection include a range of perspectives that provide concrete examples of how to approach research design, data collection, and analysis differently. No one contribution will provide a solution to our multifarious challenges, but nor should it. Our subject matter is complex and subtle, our investigations and methodological techniques will need to be equally so.

Keywords

mean --- Average --- variability --- inference --- groups --- Individuals

Comprehensive Systems Biomedicine

Authors: ---
Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889193745 Year: Pages: 113 DOI: 10.3389/978-2-88919-374-5 Language: English
Publisher: Frontiers Media SA
Subject: Genetics --- Science (General)
Added to DOAB on : 2015-11-19 16:29:12
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Systems Biomedicine is a field in perpetual development. By definition a translational discipline, it emphasizes the role of quantitative systems approaches in biomedicine and aims to offer solutions to many emerging problems characterized by levels and types of complexity and uncertainty unmet before. Many factors, including technological and societal ones, need to be considered. In particular, new technologies are providing researchers with the data deluge whose management and exploitation requires a reinvention of cross-disciplinary team efforts. The advent of “omics” and high-content imaging are examples of advances de facto establishing the necessity of systems approaches. Hypothesis-driven models and in silico validation tools in support to all the varieties of experimental applications call for a profound revision. The focus on phases like mining and assimilating the data has substantially increased so to allow for interpretable knowledge to be inferred. Notably, to be able to tackle the newly generated data dimensionality, heterogeneity and complexity, model-free and data-driven intensive applications are increasingly shaping the computational pipelines and architectures that quant specialists set aside of the high-throughput genomics, transcriptomics, proteomics platforms. As for the societal aspects, in many advanced societies health care needs now more than in the past to address the problem of managing ageing populations and their complex morbidity patterns. In parallel, there is a growing research interest on the impact that cross-disciplinary clinical, epidemiological and quantitative modelling studies can have in relation to outcomes potentially affecting the quality of life of many people. Complex systems, including those characterizing biomedicine, are assessed in both their functionality and stability, and also relatively to the capacity of generating information from diversity, variation, and complexity. Due to the combined interactions and effects, such systems embed prediction power available for instance in both target identification or marker discovery, or more generally for conducting inference about patients’ pathological states, i.e. normal versus disease, diagnostic or prognostic analysis, and preventive assessment (e.g., risk evaluation). The ultimate goal, personalized medicine, will be achieved based on the confluence of the system’s predictive power to patient-specific profiling.

Efficient Reinforcement Learning using Gaussian Processes

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Book Series: Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory ISSN: 18673813 ISBN: 9783866445697 Year: Volume: 9 Pages: IX, 205 p. DOI: 10.5445/KSP/1000019799 Language: ENGLISH
Publisher: KIT Scientific Publishing
Subject: Computer Science
Added to DOAB on : 2019-07-30 20:02:01
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This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference in nonlinear dynamic systems.First, we introduce PILCO, a fully Bayesian approach for efficient RL in continuous-valued state and action spaces when no expert knowledge is available. PILCO takes model uncertainties consistently into account during long-term planning to reduce model bias. Second, we propose principled algorithms for robust filtering and smoothing in GP dynamic systems.

The Reasoning Brain: The Interplay between Cognitive Neuroscience and Theories of Reasoning

Authors: --- --- ---
Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889451180 Year: Pages: 178 DOI: 10.3389/978-2-88945-118-0 Language: English
Publisher: Frontiers Media SA
Subject: Neurology --- Science (General)
Added to DOAB on : 2017-07-06 13:27:36
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Despite the centrality of rationality to our identity as a species (let alone the scientific endeavour), and the fact that it has been studied for several millennia, the present state of our knowledge of the mechanisms underlying logical reasoning remains highly fragmented. For example, a recent review concluded that none of the extant (12!) theories provide an adequate account (Khemlani & Johnson- Laird, 2011), while other authors argue that we are on the brink of a paradigm change, where the old binary logic framework will be washed away and replaced by more modern (and correct) probabilistic and Bayesian approaches (see for example Elqayam & Over, 2012; Oaksford & Chater, 2009; Over, 2009). Over the past 15 years neuroscience brain imaging techniques and patient studies have been used to map out the functional neuroanatomy of reasoning processes. The aim of this research topic is to discuss whether this line of research has facilitated, hindered, or has been largely irrelevant for understanding of reasoning processes. The answer is neither obvious nor uncontroversial. We would like to engage both the cognitive and the neuroscience community in this discussion. Some of the questions of interest are: How have the data generated by the patient and neuroimaging studies: • influenced our thinking about modularity of deductive reasoning • impacted the debate between mental logic theory, mental model theory and the dual mechanism accounts • affected our thinking about dual mechanism theories • informed discussion of the relationship between induction and deduction • illuminated the relationship between language, visual spatial processing and reasoning • affected our thinking about the unity of deductive reasoning processes Have any of the cognitive theories of reasoning helped us explain deficits in certain patient populations? Do certain theories do a better job of this than others? Is there any value to localizing cognitive processes and identifying dissociations (for reasoning and other cognitive processes)? What challenges have neuroimaging data raised for cognitive theories of reasoning? How can cognitive theory inform interpretation of patient data or neuroimaging data? How can patient data or neuroimaging data best inform cognitive theory? This list of questions is not exhaustive. Manuscripts addressing other related questions are welcome. We are interested in hearing from skeptics, agnostics and believers, and welcome original research contributions as well as reviews, methods, hypothesis & theory papers that contribute to the discussion of the current state of our knowledge of how neuroscience is (or is not) helping us to deepen our understanding of the mechanisms underlying logical reasoning processes.

Econometrics and Income Inequality

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ISBN: 9783038973669 9783038973676 Year: Pages: 322 DOI: 10.3390/books978-3-03897-367-6 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Social Sciences --- Business and Management
Added to DOAB on : 2018-11-26 12:04:46
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This Special Issue is devoted to the econometric analysis of income inequality and income distributions. Given the recent surge of inequality research, this Special Issue seeks to combine both theoretical and applied contributions which advance the econometric analysis of income inequality and income distributions. Possible topics include, but are not limited to, statistical inference for inequality measurement, inequality measurement with complex survey data, parametric or nonparametric modeling of income distributions, statistical decomposition methodology, methods to investigate the determinants of distributional change, causal inference in inequality measurement, and applications of such methods to substantive research questions in different fields of economics.

Fahrerabsichtserkennung und Risikobewertung für warnende Fahrerassistenzsysteme

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Book Series: Schriftenreihe / Institut für Mess- und Regelungstechnik, Karlsruher Institut für Technologie ISSN: 16134214 ISBN: 9783731505082 Year: Volume: 034 Pages: XX, 159 p. DOI: 10.5445/KSP/1000053685 Language: GERMAN
Publisher: KIT Scientific Publishing
Subject: Technology (General)
Added to DOAB on : 2019-07-30 20:02:02
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To avoid accidents, warning driver assistance systems require an on-line estimation of the current risk of collision. For that, a new method is proposed that – in principle – is able to deal with arbitrary traffic situations. This is achieved by the use of generative models to describe the expected driver behavior. Corresponding user studies in real traffic show promising results even when real time constraints are taken into account.

Computational Systems Biology of Pathogen-Host Interactions

Authors: --- ---
Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889198214 Year: Pages: 198 DOI: 10.3389/978-2-88919-821-4 Language: English
Publisher: Frontiers Media SA
Subject: Microbiology --- Science (General)
Added to DOAB on : 2016-01-19 14:05:46
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A thorough understanding of pathogenic microorganisms and their interactions with host organisms is crucial to prevent infectious threats due to the fact that Pathogen-Host Interactions (PHIs) have critical roles in initiating and sustaining infections. Therefore, the analysis of infection mechanisms through PHIs is indispensable to identify diagnostic biomarkers and next-generation drug targets and then to develop strategic novel solutions against drug-resistance and for personalized therapy. Traditional approaches are limited in capturing mechanisms of infection since they investigate hosts or pathogens individually. On the other hand, the systems biology approach focuses on the whole PHI system, and is more promising in capturing infection mechanisms. Here, we bring together studies on the below listed sections to present the current picture of the research on Computational Systems Biology of Pathogen-Host Interactions:- Computational Inference of PHI Networks using Omics Data- Computational Prediction of PHIs- Text Mining of PHI Data from the Literature- Mathematical Modeling and Bioinformatic Analysis of PHIs Computational Inference of PHI Networks using Omics Data Gene regulatory, metabolic and protein-protein networks of PHI systems are crucial for a thorough understanding of infection mechanisms. Great advances in molecular biology and biotechnology have allowed the production of related omics data experimentally. Many computational methods are emerging to infer molecular interaction networks of PHI systems from the corresponding omics data. Computational Prediction of PHIs Due to the lack of experimentally-found PHI data, many computational methods have been developed for the prediction of pathogen-host protein-protein interactions. Despite being emerging, currently available experimental PHI data are far from complete for a systems view of infection mechanisms through PHIs. Therefore, computational methods are the main tools to predict new PHIs. To this end, the development of new computational methods is of great interest. Text Mining of PHI Data from Literature Despite the recent development of many PHI-specific databases, most data relevant to PHIs are still buried in the biomedical literature, which demands for the use of text mining techniques to unravel PHIs hidden in the literature. Only some rare efforts have been performed to achieve this aim. Therefore, the development of novel text mining methods specific for PHI data retrieval is of key importance for efficient use of the available literature. Mathematical Modeling and Bioinformatic Analysis of PHIs After the reconstruction of PHI networks experimentally and/or computationally, their mathematical modeling and detailed computational analysis is required using bioinformatics tools to get insights on infection mechanisms. Bioinformatics methods are increasingly applied to analyze the increasing amount of experimentally-found and computationally-predicted PHI data. Acknowledgements: We, editors of this e-book, acknowledge Emrah Nikerel (Yeditepe University, Turkey) and Arzucan Özgür (Bogaaziçi University, Turkey) for their contributions during the initiation of the Research Topic.

Reward Processing in Motivational and Affective Disorders

Authors: ---
Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889199860 Year: Pages: 117 DOI: 10.3389/978-2-88919-986-0 Language: English
Publisher: Frontiers Media SA
Subject: Science (General) --- Psychology
Added to DOAB on : 2016-01-19 14:05:46
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Preferential reward processing is the hallmark of addiction, where salient cues become overvalued and trigger compulsion. In depression, rewards appear to lose their incentive properties or become devalued. In the context of schizophrenia, aberrations in neural reward signalling are thought to contribute to the overvaluation of irrelevant stimuli on the one hand and the onset of negative symptoms on the other. Accordingly, reward processing has emerged as a key variable in contemporary, evidence based, diagnostic frameworks, such as the Research Domain Criteria launched by the United States National Institute of Mental Health. Delineation of the underlying mechanisms of aberrant or blunted reward processing can be of trans-diagnostic importance across several neuropsychiatric disorders. Reward processing can become automatic thus raising the question of cognitive control, a core theme of this Topic, which aims at justifying the necessity of reward processing as a potential therapeutic target in clinical settings. Empirical and theoretical contributions on the following themes were expected to: *Explore new avenues of research by investigating the processing of rewards at the cognitive, behavioral, motivational, neural systems and individual difference levels. A developmental focus is promising in this regard, probing the core processes that shape reward processing and thus subsequent liability to motivational and affective disorders. *Develop and refine conceptual models of reward processing from computational neuroscience. *Promote greater understanding and development of emergent therapeutic approaches such as cognitive bias modification and behavioural approach or avoidance training. A key question is the feasibility of reversing or modifying maladaptive patterns of reward processing to therapeutic ends. *Refine and augment the evidential database for tried and tested therapies such as Contingency Management and Behavioral Activation by focusing on core cognitive processes mediating rewards. *Provide a potential dimensional approach for reward processing deficits that can be of trans-diagnostic importance in clinically relevant disorders, including depression and addiction * Investigate the subjective experience of pleasure- the hedonic aspect of reward seeking and consumption – and how this can be distinguished from the motivational, sometimes compulsive, component of reward pursuit. This promises more nuanced and effective interventions. Depression, for instance, could be seen as the restricted pursuit of pleasure rather than blunted pleasure experience; addiction can be viewed as accentuated drug seeking despite diminished consummatory pleasure. This aims to place motivation centre stage in both scenarios, emphasising the transdiagnostic theme of the Topic. *Temporal discounting of future rewards, whereby smaller, more immediate rewards are chosen even when significantly more valuable deferred rewards are available, is another trans-diagnostic phenomenon of interest in the in the present context. Factors that influence this, such as discounting of future reward are thought to reflect compulsion in the addictive context and hopelessness on the part of people experiencing depression. The executive cognitive processes that regulate this decision making are of both scientific and clinical significance. Empirical findings, theoretical contributions or commentaries bearing on cognitive or executive control were therefore welcome.

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