Gaussian Belief with dynamic data and in dynamic network

In this paper we analyse Belief Propagation over a Gaussian model in a dynamic environment. Recently, this has been proposed as a method to average local measurement values by a distributed protocol ("Consensus Propagation", Moallemi & Van Roy, 2006), where the average is available for read-out at every single node. In the case that the underlying network is constant but the values to be averaged fluctuate ("dynamic data"), convergence and accuracy are determined by the spectral properties of an associated Ruelle-Perron-Frobenius operator. For Gaussian models on Erdos-Renyi graphs, numerical computation points to a spectral gap remaining in the large-size limit, implying exceptionally good scalability. In a model where the underlying network also fluctuates ("dynamic network"), averaging is more effective than in the dynamic data case. Altogether, this implies very good performance of these methods in very large systems, and opens a new field of statistical physics of large (and dynamic) information systems.Comment: 5 pages, 7 figure

Hierarchical Models for Independence Structures of Networks

We introduce a new family of network models, called hierarchical network models, that allow us to represent in an explicit manner the stochastic dependence among the dyads (random ties) of the network. In particular, each member of this family can be associated with a graphical model defining conditional independence clauses among the dyads of the network, called the dependency graph. Every network model with dyadic independence assumption can be generalized to construct members of this new family. Using this new framework, we generalize the Erd\"os-R\'enyi and beta-models to create hierarchical Erd\"os-R\'enyi and beta-models. We describe various methods for parameter estimation as well as simulation studies for models with sparse dependency graphs.Comment: 19 pages, 7 figure

Large Area Crop Inventory Experiment (LACIE). Intensive test site assessment report

There are no author-identified significant results in this report

Detection Prospects for Majorana Fermion WIMPless Dark Matter

We consider both velocity-dependent and velocity-independent contributions to spin-dependent (SD) and spin-independent (SI) nuclear scattering (including one-loop corrections) of WIMPless dark matter, in the case where the dark matter candidate is a Majorana fermion. We find that spin-independent scattering arises only from the mixing of exotic squarks, or from velocity-dependent terms. Nevertheless (and contrary to the case of MSSM neutralino WIMPs), we find a class of models which cannot be detected through SI scattering, but can be detected at IceCube/DeepCore through SD scattering. We study the detection prospects for both SI and SD detection strategies for a large range of Majorana fermion WIMPless model parameters.Comment: 14 pages, 3 figures. v2: updated to match published versio

Apparent Predation by Gray Jays, Perisoreus canadensis, on Long-toed Salamanders, Ambystoma macrodactylum, in the Oregon Cascade Range

We report observations of Gray Jays (Perisoreus canadensis) appearing to consume larval Long-toed Salamanders (Ambystoma macrodactylum) in a drying subalpine pond in Oregon, USA. Corvids are known to prey upon a variety of anuran amphibians, but to our knowledge, this is the first report of predation by any corvid on aquatic salamanders. Long-toed Salamanders appear palatable to Gray Jays, and may provide a food resource to Gray Jays when salamander larvae are concentrated in drying temporary ponds

Analysis of tender sum forecasting by quantity surveyors and contractors in South Africa

Although extensive research has been undertaken on the accuracy of quantity surveyors' tender price forecasts, very little of this research contains information relating to the factors affecting tender sum forecasting by quantity surveyors and contractors. The primary objective of this empirical study was to gain insight into the factors influencing both quantity surveyors' and contractors' tender price forecasts. This was achieved through an analysis of tender information relating to 278 projects for a fifteen-year period and collected from 30 quantity-surveying practices and MBATA tender records. The analysis of South African tender information reported in this article indicates an average forecast performance by quantity surveyors of 8.33% (std dev (standard deviation) = 11,183, CV (coefficient of variation) = 134,2%). The variability of contractors' tenders ranged from 0,37% to 46,53%, with a mean of 5,65% (std dev = 5,22, SE (standard error) = 0,313). Furthermore, there is no evidence to suggest that forecast performance is dependent on type of project, client, function of project, size of project, location of project and number of bidders. The contractor's results suggest that local authority projects are associated with high variability of their tender sum forecasts. The only factor, which shows significance for quantity surveyors, is the date of tender which may tend to point to the importance of market conditions and economic cycle in the tender sum forecast performance of South African quantity surveyors

Picturing classical and quantum Bayesian inference

We introduce a graphical framework for Bayesian inference that is sufficiently general to accommodate not just the standard case but also recent proposals for a theory of quantum Bayesian inference wherein one considers density operators rather than probability distributions as representative of degrees of belief. The diagrammatic framework is stated in the graphical language of symmetric monoidal categories and of compact structures and Frobenius structures therein, in which Bayesian inversion boils down to transposition with respect to an appropriate compact structure. We characterize classical Bayesian inference in terms of a graphical property and demonstrate that our approach eliminates some purely conventional elements that appear in common representations thereof, such as whether degrees of belief are represented by probabilities or entropic quantities. We also introduce a quantum-like calculus wherein the Frobenius structure is noncommutative and show that it can accommodate Leifer's calculus of `conditional density operators'. The notion of conditional independence is also generalized to our graphical setting and we make some preliminary connections to the theory of Bayesian networks. Finally, we demonstrate how to construct a graphical Bayesian calculus within any dagger compact category.Comment: 38 pages, lots of picture

Quantifying Self-Organization with Optimal Predictors

Despite broad interest in self-organizing systems, there are few quantitative, experimentally-applicable criteria for self-organization. The existing criteria all give counter-intuitive results for important cases. In this Letter, we propose a new criterion, namely an internally-generated increase in the statistical complexity, the amount of information required for optimal prediction of the system's dynamics. We precisely define this complexity for spatially-extended dynamical systems, using the probabilistic ideas of mutual information and minimal sufficient statistics. This leads to a general method for predicting such systems, and a simple algorithm for estimating statistical complexity. The results of applying this algorithm to a class of models of excitable media (cyclic cellular automata) strongly support our proposal.Comment: Four pages, two color figure

Binary Models for Marginal Independence

Log-linear models are a classical tool for the analysis of contingency tables. In particular, the subclass of graphical log-linear models provides a general framework for modelling conditional independences. However, with the exception of special structures, marginal independence hypotheses cannot be accommodated by these traditional models. Focusing on binary variables, we present a model class that provides a framework for modelling marginal independences in contingency tables. The approach taken is graphical and draws on analogies to multivariate Gaussian models for marginal independence. For the graphical model representation we use bi-directed graphs, which are in the tradition of path diagrams. We show how the models can be parameterized in a simple fashion, and how maximum likelihood estimation can be performed using a version of the Iterated Conditional Fitting algorithm. Finally we consider combining these models with symmetry restrictions

Degree of explanation

Partial explanations are everywhere. That is, explanations citing causes that explain some but not all of an effect are ubiquitous across science, and these in turn rely on the notion of degree of explanation. I argue that current accounts are seriously deficient. In particular, they do not incorporate adequately the way in which a cause’s explanatory importance varies with choice of explanandum. Using influential recent contrastive theories, I develop quantitative definitions that remedy this lacuna, and relate it to existing measures of degree of causation. Among other things, this reveals the precise role here of chance, as well as bearing on the relation between causal explanation and causation itself