Sampling decomposable graphs using a Markov chain on junction trees

Full Bayesian computational inference for model determination in undirected graphical models is currently restricted to decomposable graphs, except for problems of very small scale. In this paper we develop new, more efficient methodology for such inference, by making two contributions to the computational geometry of decomposable graphs. The first of these provides sufficient conditions under which it is possible to completely connect two disconnected complete subsets of vertices, or perform the reverse procedure, yet maintain decomposability of the graph. The second is a new Markov chain Monte Carlo sampler for arbitrary positive distributions on decomposable graphs, taking a junction tree representing the graph as its state variable. The resulting methodology is illustrated with numerical experiments on three specific models.Comment: 22 pages, 7 figures, 1 table. V2 as V1 except that Fig 1 was corrected. V3 has significant edits, dropping some figures and including additional examples and a discussion of the non-decomposable case. V4 is further edited following review, and includes additional reference

Conditions for open source as a signalling device

Open source projects produce goods or standards that do not allow for the appropriation of private returns by those who contribute to their production. In this paper we analyze why programmers will nevertheless invest their time and effort to code open source software. We argue that the particular way in which open source projects are managed and especially how contributions are attributed to individual agents, allows the best programmers to create a signal that more mediocre programmers cannot achieve. Through setting themselves apart they can turn this signal into monetary rewards that correspond to their superior capabilities. With this incentive they will forgo the immediate rewards they could earn in software companies producing proprietary software by restricting the access to the source code of their product. Whenever institutional arrangements are in place that enable the acquisition of such a signal and the subsequent substitution into monetary rewards, the contribution to open source projects and the resulting public good is a feasible outcome that can be explained by standard economic theory

Open source as a signalling device : an economic analysis

Open source projects produce goods or standards that do not allow for the appropriation of private returns by those who contribute to their production. In this paper we analyze why programmers will nevertheless invest their time and effort to code open source software. We argue that the particular way in which open source projects are managed and especially how contributions are attributed to individual agents, allows the best programmers to create a signal that more mediocre programmers cannot achieve. Through setting themselves apart they can turn this signal into monetary rewards that correspond to their superior capabilities. With this incentive they will forgo the immediate rewards they could earn in software companies producing proprietary software by restricting the access to the source code of their product. Whenever institutional arrangements are in place that enable the acquisition of such a signal and the subsequent substitution into monetary rewards, the contribution to open source projects and the resulting public good is a feasible outcome that can be explained by standard economic theory

The Pan-STARRS Moving Object Processing System

We describe the Pan-STARRS Moving Object Processing System (MOPS), a modern software package that produces automatic asteroid discoveries and identifications from catalogs of transient detections from next-generation astronomical survey telescopes. MOPS achieves > 99.5% efficiency in producing orbits from a synthetic but realistic population of asteroids whose measurements were simulated for a Pan-STARRS4-class telescope. Additionally, using a non-physical grid population, we demonstrate that MOPS can detect populations of currently unknown objects such as interstellar asteroids. MOPS has been adapted successfully to the prototype Pan-STARRS1 telescope despite differences in expected false detection rates, fill-factor loss and relatively sparse observing cadence compared to a hypothetical Pan-STARRS4 telescope and survey. MOPS remains >99.5% efficient at detecting objects on a single night but drops to 80% efficiency at producing orbits for objects detected on multiple nights. This loss is primarily due to configurable MOPS processing limits that are not yet tuned for the Pan-STARRS1 mission. The core MOPS software package is the product of more than 15 person-years of software development and incorporates countless additional years of effort in third-party software to perform lower-level functions such as spatial searching or orbit determination. We describe the high-level design of MOPS and essential subcomponents, the suitability of MOPS for other survey programs, and suggest a road map for future MOPS development.Comment: 57 Pages, 26 Figures, 13 Table

An economic evaluation of the potential for distributed energy in Australia

Australia’s Commonwealth Scientific and Industrial Research Organisation (CSIRO) recently completed a major study investigating the value of distributed energy (DE; collectively demand management, energy efficiency and distributed generation) technologies for reducing greenhouse gas emissions from Australia’s energy sector (CSIRO, 2009). This comprehensive report covered potential economic, environmental, technical, social, policy and regulatory impacts that could result from the wide scale adoption of these technologies. In this paper we highlight the economic findings from the study. Partial Equilibrium modeling of the stationary and transport sectors found that Australia could achieve a present value welfare gain of around $130 billion when operating under a 450 ppm carbon reduction trajectory through to 2050. Modeling also suggests that reduced volatility in the spot market could decrease average prices by up to 12% in 2030 and 65% in 2050 by using local resources to better cater for an evolving supply-demand imbalance. Further modeling suggests that even a small amount of distributed generation located within a distribution network has the potential to significantly alter electricity prices by changing the merit order of dispatch in an electricity spot market. Changes to the dispatch relative to a base case can have both positive and negative effects on network losses.Distributed energy; Economic modeling; Carbon price; Electricity markets