A Statistical Social Network Model for Consumption Data in Food Webs

We adapt existing statistical modeling techniques for social networks to study consumption data observed in trophic food webs. These data describe the feeding volume (non-negative) among organisms grouped into nodes, called trophic species, that form the food web. Model complexity arises due to the extensive amount of zeros in the data, as each node in the web is predator/prey to only a small number of other trophic species. Many of the zeros are regarded as structural (non-random) in the context of feeding behavior. The presence of basal prey and top predator nodes (those who never consume and those who are never consumed, with probability 1) creates additional complexity to the statistical modeling. We develop a special statistical social network model to account for such network features. The model is applied to two empirical food webs; focus is on the web for which the population size of seals is of concern to various commercial fisheries.Comment: On 2013-09-05, a revised version entitled "A Statistical Social Network Model for Consumption Data in Trophic Food Webs" was accepted for publication in the upcoming Special Issue "Statistical Methods for Ecology" in the journal Statistical Methodolog

A CasADi Based Toolchain For JModelica.org

Computer-aided modeling for simulation, optimization and analysis is increasingly used for product development in industry today, resulting in high demands on the tools used. A tool chain for transferring interpreted code of the modeling languages Modelica and Optimica from the simulation and optimization tool JModelica.org to CasADi has been implemented. CasADi provides several desirable features, most importantly an integrated and ecient automatic dierentiation engine and the ability to interactively work with the systems expressed using it. The biggest problems solved to enable this were the creation of a representation of the mathematical systems described by Modelica and Optimica code that is integrated with CasADi, and the construction of a transfer scheme for moving information from the Java-based JModelica.org compiler to C++ in which CasADi resides. This was successfully achieved for a continuous subset of Modelica and Optimica that may contain functions

Tools for distributed application management

Distributed application management consists of monitoring and controlling an application as it executes in a distributed environment. It encompasses such activities as configuration, initialization, performance monitoring, resource scheduling, and failure response. The Meta system is described: a collection of tools for constructing distributed application management software. Meta provides the mechanism, while the programmer specifies the policy for application management. The policy is manifested as a control program which is a soft real time reactive program. The underlying application is instrumented with a variety of built-in and user defined sensors and actuators. These define the interface between the control program and the application. The control program also has access to a database describing the structure of the application and the characteristics of its environment. Some of the more difficult problems for application management occur when pre-existing, nondistributed programs are integrated into a distributed application for which they may not have been intended. Meta allows management functions to be retrofitted to such programs with a minimum of effort

Compositional Model Repositories via Dynamic Constraint Satisfaction with Order-of-Magnitude Preferences

The predominant knowledge-based approach to automated model construction, compositional modelling, employs a set of models of particular functional components. Its inference mechanism takes a scenario describing the constituent interacting components of a system and translates it into a useful mathematical model. This paper presents a novel compositional modelling approach aimed at building model repositories. It furthers the field in two respects. Firstly, it expands the application domain of compositional modelling to systems that can not be easily described in terms of interacting functional components, such as ecological systems. Secondly, it enables the incorporation of user preferences into the model selection process. These features are achieved by casting the compositional modelling problem as an activity-based dynamic preference constraint satisfaction problem, where the dynamic constraints describe the restrictions imposed over the composition of partial models and the preferences correspond to those of the user of the automated modeller. In addition, the preference levels are represented through the use of symbolic values that differ in orders of magnitude

Bayesian regression for network data

The research contained in this dissertation extends modeling methods for network data. Networks are widely used, across a number of disciplines, to represent objects and their interconnectedness. The prevalence of this data structure outlines just one of our motivations for developing novel modeling methods and computational tools that improve our understanding of network-indexed data. We first consider the problem of statistical inference and prediction for processes defined on networks. We assume that the network of interest is known, and we would like to learn more about an attribute associated with its vertices. Drawing on ideas from functional data analysis, our proposed model consists of node indexed predictors and a basis expansion of their coefficients, allowing the coefficients to vary over the network. We employ a regularization procedure, cast as a prior distribution on the regression coefficients in a Bayesian setup, so that predicted responses vary smoothly according to the topology of the network. We present a novel variable selection technique, introduce efficient expectation-maximization fitting algorithms and Markov Chain Monte Carlo sampling schemes, and provide computationally-friendly methods for eliciting hyper-prior parameters. Turning to an application, we study occurrences of residential burglary in Boston, Massachusetts. Noting that crime rates are not spatially homogeneous, and that rates appear to vary sharply across regions or hot zones in the city, we construct a hierarchical model that addresses these issues and gives insight into the spatial patterns and dynamics of residential burglary in Boston. Finally, we address the computational challenges of performing inference on network structure. With the goal of understanding the processes behind edge formulation within a network of given size, we present algorithms and data representations that allow for more efficient inference on large-scale networks. Through a regression framework, the tools allow for investigating a variety of effects that may shape a network's structure, such as degree heterogeneity and clustering. We illustrate and evaluate the benefits of our work on both simulated and real-world networks. Finally, with the goal of exploring the relationship between a set of predictor variables and a vertex-pair indexed response, we introduce a flexible approach to modeling network ties. Through a generalized linear model framework, we are able to model weighted and binary edges while investigating a variety of effects or features commonly found in networks. We present algorithms and data representations that allow for efficient inference, and we illustrate and evaluate the benefits of our work on both simulated and real-world networks