Simulation of truncated normal variables

We provide in this paper simulation algorithms for one-sided and two-sided truncated normal distributions. These algorithms are then used to simulate multivariate normal variables with restricted parameter space for any covariance structure.Comment: This 1992 paper appeared in 1995 in Statistics and Computing and the gist of it is contained in Monte Carlo Statistical Methods (2004), but I receive weekly requests for reprints so here it is

Anaerobic digestion of whole-crop winter wheat silage for renewable energy production

With biogas production expanding across Europe in response to renewable energy incentives, a wider variety of crops need to be considered as feedstock. Maize, the most commonly used crop at present, is not ideal in cooler, wetter regions, where higher energy yields per hectare might be achieved with other cereals. Winter wheat is a possible candidate because, under these conditions, it has a good biomass yield, can be ensiled, and can be used as a whole crop material. The results showed that, when harvested at the medium milk stage, the specific methane yield was 0.32 m3 CH4 kg–1 volatile solids added, equal to 73% of the measured calorific value. Using crop yield values for the north of England, a net energy yield of 146–155 GJ ha–1 year–1 could be achieved after taking into account both direct and indirect energy consumption in cultivation, processing through anaerobic digestion, and spreading digestate back to the land. The process showed some limitations, however: the relatively low density of the substrate made it difficult to mix the digester, and there was a buildup of soluble chemical oxygen demand, which represented a loss in methane potential and may also have led to biofoaming. The high nitrogen content of the wheat initially caused problems, but these could be overcome by acclimatization. A combination of these factors is likely to limit the loading that can be applied to the digester when using winter wheat as a substrat

Using Markov chain Monte Carlo methods for estimating parameters with gravitational radiation data

We present a Bayesian approach to the problem of determining parameters for coalescing binary systems observed with laser interferometric detectors. By applying a Markov Chain Monte Carlo (MCMC) algorithm, specifically the Gibbs sampler, we demonstrate the potential that MCMC techniques may hold for the computation of posterior distributions of parameters of the binary system that created the gravity radiation signal. We describe the use of the Gibbs sampler method, and present examples whereby signals are detected and analyzed from within noisy data.Comment: 21 pages, 10 figure

Extracting galactic binary signals from the first round of Mock LISA Data Challenges

We report on the performance of an end-to-end Bayesian analysis pipeline for detecting and characterizing galactic binary signals in simulated LISA data. Our principal analysis tool is the Blocked-Annealed Metropolis Hasting (BAM) algorithm, which has been optimized to search for tens of thousands of overlapping signals across the LISA band. The BAM algorithm employs Bayesian model selection to determine the number of resolvable sources, and provides posterior distribution functions for all the model parameters. The BAM algorithm performed almost flawlessly on all the Round 1 Mock LISA Data Challenge data sets, including those with many highly overlapping sources. The only misses were later traced to a coding error that affected high frequency sources. In addition to the BAM algorithm we also successfully tested a Genetic Algorithm (GA), but only on data sets with isolated signals as the GA has yet to be optimized to handle large numbers of overlapping signals.Comment: 13 pages, 4 figures, submitted to Proceedings of GWDAW-11 (Berlin, Dec. '06

Non-linear regression models for Approximate Bayesian Computation

Approximate Bayesian inference on the basis of summary statistics is well-suited to complex problems for which the likelihood is either mathematically or computationally intractable. However the methods that use rejection suffer from the curse of dimensionality when the number of summary statistics is increased. Here we propose a machine-learning approach to the estimation of the posterior density by introducing two innovations. The new method fits a nonlinear conditional heteroscedastic regression of the parameter on the summary statistics, and then adaptively improves estimation using importance sampling. The new algorithm is compared to the state-of-the-art approximate Bayesian methods, and achieves considerable reduction of the computational burden in two examples of inference in statistical genetics and in a queueing model.Comment: 4 figures; version 3 minor changes; to appear in Statistics and Computin

Randomized, Controlled Trial of the Long Term Safety, Immunogenicity and Efficacy of RTS,S/AS02(D) Malaria Vaccine in Infants Living in a Malaria-Endemic Region.

The RTS,S/AS malaria candidate vaccine is being developed with the intent to be delivered, if approved, through the Expanded Programme on Immunization (EPI) of the World Health Organization. Safety, immunogenicity and efficacy of the RTS,S/AS02(D) vaccine candidate when integrated into a standard EPI schedule for infants have been reported over a nine-month surveillance period. This paper describes results following 20 months of follow up. This Phase IIb, single-centre, randomized controlled trial enrolled 340 infants in Tanzania to receive three doses of RTS,S/AS02(D) or hepatitis B vaccine at 8, 12, and 16 weeks of age. All infants also received DTPw/Hib (diphtheria and tetanus toxoids, whole-cell pertussis vaccine, conjugated Haemophilus influenzae type b vaccine) at the same timepoints. The study was double-blinded to month 9 and single-blinded from months 9 to 20. From month 0 to 20, at least one SAE was reported in 57/170 infants who received RTS,S/AS02(D) (33.5%; 95% confidence interval [CI]: 26.5, 41.2) and 62/170 infants who received hepatitis B vaccine (36.5%; 95% CI: 29.2, 44.2). The SAE profile was similar in both vaccine groups; none were considered to be related to vaccination. At month 20, 18 months after completion of vaccination, 71.8% of recipients of RTS,S/AS02(D) and 3.8% of recipients of hepatitis B vaccine had seropositive titres for anti-CS antibodies; seroprotective levels of anti-HBs antibodies remained in 100% of recipients of RTS,S/AS02(D) and 97.7% recipients of hepatitis B vaccine. Anti-HBs antibody GMTs were higher in the RTS,S/AS02(D) group at all post-vaccination time points compared to control. According to protocol population, vaccine efficacy against multiple episodes of malaria disease was 50.7% (95% CI: -6.5 to 77.1, p = 0.072) and 26.7% (95% CI: -33.1 to 59.6, p = 0.307) over 12 and 18 months post vaccination, respectively. In the Intention to Treat population, over the 20-month follow up, vaccine efficacy against multiple episodes of malaria disease was 14.4% (95% CI: -41.9 to 48.4, p = 0.545). The acceptable safety profile and good tolerability of RTS,S/AS02(D) in combination with EPI vaccines previously reported from month 0 to 9 was confirmed over a 20 month surveillance period in this infant population. Antibodies against both CS and HBsAg in the RTS,S/AS02(D) group remained significantly higher compared to control for the study duration. Over 18 months follow up, RTS,S/AS02(D) prevented approximately a quarter of malaria cases in the study population. CLINICAL TRIALS: Gov identifier: NCT00289185

II: Bayesian Methods for Cosmological Parameter Estimation from Cosmic Microwave Background Measurements

We present a strategy for a statistically rigorous Bayesian approach to the problem of determining cosmological parameters from the results of observations of anisotropies in the cosmic microwave background. Our strategy relies on Markov chain Monte Carlo methods, specifically the Metropolis-Hastings algorithm, to perform the necessary high-dimensional integrals. We describe the Metropolis-Hastings algorithm in detail and discuss the results of our test on simulated data.Comment: This paper expands on the work presented in astro-ph/0006401, and now includes a test of the method. 15 pages, 1 figur

Mapping Dynamic Histone Acetylation Patterns to Gene Expression in Nanog-depleted Murine Embryonic Stem Cells

Embryonic stem cells (ESC) have the potential to self-renew indefinitely and to differentiate into any of the three germ layers. The molecular mechanisms for self-renewal, maintenance of pluripotency and lineage specification are poorly understood, but recent results point to a key role for epigenetic mechanisms. In this study, we focus on quantifying the impact of histone 3 acetylation (H3K9,14ac) on gene expression in murine embryonic stem cells. We analyze genome-wide histone acetylation patterns and gene expression profiles measured over the first five days of cell differentiation triggered by silencing Nanog, a key transcription factor in ESC regulation. We explore the temporal and spatial dynamics of histone acetylation data and its correlation with gene expression using supervised and unsupervised statistical models. On a genome-wide scale, changes in acetylation are significantly correlated to changes in mRNA expression and, surprisingly, this coherence increases over time. We quantify the predictive power of histone acetylation for gene expression changes in a balanced cross-validation procedure. In an in-depth study we focus on genes central to the regulatory network of Mouse ESC, including those identified in a recent genome-wide RNAi screen and in the PluriNet, a computationally derived stem cell signature. We find that compared to the rest of the genome, ESC-specific genes show significantly more acetylation signal and a much stronger decrease in acetylation over time, which is often not reflected in an concordant expression change. These results shed light on the complexity of the relationship between histone acetylation and gene expression and are a step forward to dissect the multilayer regulatory mechanisms that determine stem cell fate.Comment: accepted at PLoS Computational Biolog

Designing multifunctional landscapes for forest conservation

A multifunctional landscape approach to forest protection has been advocated for tropical countries. Designing such landscapes necessitates that the role of different land uses in protecting forest be evaluated, along with the spatial interactions between land uses. However, such evaluations have been hindered by a lack of suitable analysis methodologies and data with fine spatial resolution over long time periods. We demonstrate the utility of a matching method with multiple categories to evaluate the role of alternative land uses in protecting forest. We also assessed the impact of land use change trajectories on the rate of deforestation. We employed data from Kalimantan (Indonesian Borneo) at three different time periods during 2000–2012 to illustrate our approach. Four single land uses (protected areas (PA), natural forest logging concessions (LC), timber plantation concessions (TC) and oil-palm plantation concessions (OC)) and two mixed land uses (mixed concessions and the overlap between concessions and PA) were assessed. The rate of deforestation was found to be lowest for PA, followed by LC. Deforestation rates for all land uses tended to be highest for locations that share the characteristics of areas in which TC or OC are located (e.g. degraded areas), suggesting that these areas are inherently more susceptible to deforestation due to foregone opportunities. Our approach provides important insights into how multifunctional landscapes can be designed to enhance the protection of biodiversity