1,915 research outputs found
Social Network Analysis with sna
Modern social network analysis---the analysis of relational data arising from social systems---is a computationally intensive area of research. Here, we provide an overview of a software package which provides support for a range of network analytic functionality within the R statistical computing environment. General categories of currently supported functionality are described, and brief examples of package syntax and usage are shown.
Social Network Analysis with sna
Modern social network analysis---the analysis of relational data arising from social systems---is a computationally intensive area of research. Here, we provide an overview of a software package which provides support for a range of network analytic functionality within the R statistical computing environment. General categories of currently supported functionality are described, and brief examples of package syntax and usage are shown
Causal Inference from Statistical Data
The so-called kernel-based tests of independence are developed for automatic causal discovery between random variables from purely observational statistical data, i.e., without intervention. Beyond the independence relations, the complexity of conditional distriubtions is used as an additional inference principle of determining the causal ordering between variables. Experiments with simulated and real-world data show that the proposed methods surpass the state-of-the-art approaches
Algebraic Statistics in Practice: Applications to Networks
Algebraic statistics uses tools from algebra (especially from multilinear
algebra, commutative algebra and computational algebra), geometry and
combinatorics to provide insight into knotty problems in mathematical
statistics. In this survey we illustrate this on three problems related to
networks, namely network models for relational data, causal structure discovery
and phylogenetics. For each problem we give an overview of recent results in
algebraic statistics with emphasis on the statistical achievements made
possible by these tools and their practical relevance for applications to other
scientific disciplines
A new class of neural architectures to model episodic memory : computational studies of distal reward learning
A computational cognitive neuroscience model is proposed, which models episodic memory based on the mammalian brain. A computational neural architecture instantiates the proposed model and is tested on a particular task of distal reward learning. Categorical Neural Semantic Theory informs the architecture design. To experiment upon the computational brain model, embodiment and an environment in which the embodiment exists are simulated. This simulated environment realizes the Morris Water Maze task, a well established biological experimental test of distal reward learning. The embodied neural architecture is treated as a virtual rat and the environment it acts in as a virtual water tank. Performance levels of the neural architectures are evaluated through analysis of embodied behavior in the distal reward learning task. Comparison is made to biological rat experimental data, as well as comparison to other published models. In addition, differences in performance are compared between the normal and categorically informed versions of the architecture
Why Philosophers Should Care About Computational Complexity
One might think that, once we know something is computable, how efficiently
it can be computed is a practical question with little further philosophical
importance. In this essay, I offer a detailed case that one would be wrong. In
particular, I argue that computational complexity theory---the field that
studies the resources (such as time, space, and randomness) needed to solve
computational problems---leads to new perspectives on the nature of
mathematical knowledge, the strong AI debate, computationalism, the problem of
logical omniscience, Hume's problem of induction, Goodman's grue riddle, the
foundations of quantum mechanics, economic rationality, closed timelike curves,
and several other topics of philosophical interest. I end by discussing aspects
of complexity theory itself that could benefit from philosophical analysis.Comment: 58 pages, to appear in "Computability: G\"odel, Turing, Church, and
beyond," MIT Press, 2012. Some minor clarifications and corrections; new
references adde
Application of lower quantiles for complete lattices to ranking data: analyzing outlyingness of preference orderings
The aim of the present paper is to apply a recently developed quantile approach for lattice-valued data to the special case of ranking data. We show how to analyze profiles of total orders by means of lattice-valued quantiles and thereby develop new methods of descriptive data analysis for ranking data beyond known methods like permutation polytopes or multidimensional scaling. We furthermore develop an aggregation rule for social profiles (, called commonality sharing, here,) that selects from a given profile that ordering(s) that is (are) most centered in the profile, where the notion of centrality and outlyingness are based on purely order-theoretic concepts. Finally, we sketch, how one can use the quantile approach to establish tests of model fit for statistical models of ranking data
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