Sines, steps and droplets: Semiparametric Bayesian modeling of arrival time series

I describe ongoing work developing Bayesian methods for flexible modeling of arrival time series data without binning, aiming to improve detection and measurement of X-ray and gamma-ray pulsars, and of pulses in gamma-ray bursts. The methods use parametric and semiparametric Poisson point process models for the event rate, and by design have close connections to conventional frequentist methods currently used in time-domain astronomy.Comment: 4 pages, 1 figure; to appear in the proceedings of IAU Symposium 285, "New Horizons in Time Domain Astronomy" (proceedings eds. Elizabeth Griffin, Bob Hanisch, and Rob Seaman), Cambridge University Press; see http://www.physics.ox.ac.uk/IAUS285

Introduction to papers on astrostatistics

We are pleased to present a Special Section on Statistics and Astronomy in this issue of the The Annals of Applied Statistics. Astronomy is an observational rather than experimental science; as a result, astronomical data sets both small and large present particularly challenging problems to analysts who must make the best of whatever the sky offers their instruments. The resulting statistical problems have enormous diversity. In one problem, one may have to carefully quantify uncertainty in a hard-won, sparse data set; in another, the sheer volume of data may forbid a formally optimal analysis, requiring judicious balancing of model sophistication, approximations, and clever algorithms. Often the data bear a complex relationship to the underlying phenomenon producing them, much in the manner of inverse problems.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS234 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Wetland-based passive treatment systems for gold ore processing effluents containing residual cyanide, metals and nitrogen species

Gold extraction operations generate a variety of wastes requiring responsible disposal in compliance with current environmental regulations. During recent decades, increased emphasis has been placed on effluent control and treatment, in order to avoid the threat to the environment posed by toxic constituents. In many modern gold mining and ore processing operations, cyanide species are of most immediate concern. Given that natural degradation processes are known to reduce the toxicity of cyanide over time, trials have been made at laboratory and field scales into the feasibility of using wetland-based passive systems as low-cost and environmentally friendly methods for long-term treatment of leachates from closed gold mine tailing disposal facilities. Laboratory experiments on discrete aerobic and anaerobic treatment units supported the development of design parameters for the construction of a field-scale passive system at a gold mine site in northern Spain. An in situ pilot-scale wetland treatment system was designed, constructed and monitored over a nine-month period. Overall, the results suggest that compost-based constructed wetlands are capable of detoxifying cyanidation effluents, removing about 21.6% of dissolved cyanide and 98% of Cu, as well as nitrite and nitrate. Wetland-based passive systems can therefore be considered as a viable technology for removal of residual concentrations of cyanide from leachates emanating from closed gold mine tailing disposal facilities

Bayesian Adaptive Exploration

I describe a framework for adaptive scientific exploration based on iterating an Observation--Inference--Design cycle that allows adjustment of hypotheses and observing protocols in response to the results of observation on-the-fly, as data are gathered. The framework uses a unified Bayesian methodology for the inference and design stages: Bayesian inference to quantify what we have learned from the available data and predict future data, and Bayesian decision theory to identify which new observations would teach us the most. When the goal of the experiment is simply to make inferences, the framework identifies a computationally efficient iterative ``maximum entropy sampling'' strategy as the optimal strategy in settings where the noise statistics are independent of signal properties. Results of applying the method to two ``toy'' problems with simulated data--measuring the orbit of an extrasolar planet, and locating a hidden one-dimensional object--show the approach can significantly improve observational efficiency in settings that have well-defined nonlinear models. I conclude with a list of open issues that must be addressed to make Bayesian adaptive exploration a practical and reliable tool for optimizing scientific exploration.Comment: 17 pages, 5 figure