Statistical inference of OH concentrations and air mass dilution rates from successive observations of non-methane hydrocarbons in single air masses

Bayesian inference has been used to determine rigorous estimates of hydroxyl radical concentrations () and air mass dilution rates (K) averaged following air masses between linked observations of nonmethane hydrocarbons (NMHCs) spanning the North Atlantic during the Intercontinental Transport and Chemical Transformation (ITCT)-Lagrangian-2K4 experiment. The Bayesian technique obtains a refined (posterior) distribution of a parameter given data related to the parameter through a model and prior beliefs about the parameter distribution. Here, the model describes hydrocarbon loss through OH reaction and mixing with a background concentration at rate K. The Lagrangian experiment provides direct observations of hydrocarbons at two time points, removing assumptions regarding composition or sources upstream of a single observation. The estimates are sharpened by using many hydrocarbons with different reactivities and accounting for their variability and measurement uncertainty. A novel technique is used to construct prior background distributions of many species, described by variation of a single parameter . This exploits the high correlation of species, related by the first principal component of many NMHC samples. The Bayesian method obtains posterior estimates of , K and following each air mass. Median values are typically between 0.5 and 2.0 × 106 molecules cm−3, but are elevated to between 2.5 and 3.5 × 106 molecules cm−3, in low-level pollution. A comparison of estimates from absolute NMHC concentrations and NMHC ratios assuming zero background (the “photochemical clock” method) shows similar distributions but reveals systematic high bias in the estimates from ratios. Estimates of K are ∼0.1 day−1 but show more sensitivity to the prior distribution assumed.

Producing Inequalities: Regional Sequences in Later Prehistoric Southern Spain

This paper presents regional sequences of production, consumption and Social relations ill Southern Spain from the beginning of the Neolithic to the Early Bronze Age (c. 5600-1550 BC). The regions Studied are southeast Spain, Valencia, the southern Meseta and central/western Andalucia. The details presented for each region and period vary in quality but Show how Much our knowledge of the archaeological record of southern Spain has changed during the last four decades. Among the Surprises are the rapidity of agricultural adoption. the emergence of regional centres of aggregated population in enclosed/fortified settlements of up to 400 hectares in the fourth and third millennia BC. the use of copper objects as instruments of production, rather than as items With 11 purely symbolic of 'prestige' value, large-scale copper production in western Andalucia in the third millennium BC (as opposed to the usual domestic production model), and the inference of societies based oil relations of class.

Changes in the extent of glaciers in the Eastern Terskey Alatoo, the Central Tien-Shan, in response to climatic fluctuations in between the end of the 19th and the beginning of the 21st century

Changes in the extent of glaciers and rates of glacier termini retreat in the eastern Terskey-Alatoo Range, the Tien Shan Mountains, Central Asia have been evaluated using the remote sensing techniques. Changes in the extent of 335 glaciers between the end of the Little Ice Age (LIA; mid-19th century), 1990 and 2003 have been estimated through the delineation of glacier outlines and the LIA moraine positions on the Landsat TM and ASTER imagery for 1990 and 2003 respectively. By 2003, the glacier surface area had decreased by 19% of the LIA value, which constitutes a 76 km(2) reduction in glacier surface area. Mapping of 109 glaciers using the 1965 1:25,000 maps revealed that glacier surface area decreased by 12.6% of the 1965 value between 1965 and 2003. Detailed mapping of 10 glaciers using historical maps and aerial photographs from the 1943-1977 period, has enabled glacier extent variations over the 20th century to be identified with a higher temporal resolution. Glacial retreat was slow in the early 20th century but increased considerably between 1943 and 1956 and then again after 1977. The post-1990 period has been marked by the most rapid glacier retreat since the end of the LIA. The observed changes in the extent of glaciers are in line with the observed climatic warming. The regional weather stations have revealed a strong climatic warming during the ablation season since the 1950s at a rate of 0.02-0.03 degrees Ca-1. At the higher elevations in the study area represented by the Tien Shan meteorological station, the summer warming was accompanied by negative anomalies in annual precipitation in the 1990s enhancing glacier retreat. However, trends in precipitation in the post-1997 period cannot be evaluated due to the change in observational practices at this station. Neither station in the study area exhibits significant long-term trends in precipitation. Crown Copyright (C) 2009 Published by Elsevier B.V. All rights reserved.

Skill Rating by Bayesian Inference

Systems Engineering often involves computer modelling the behaviour of proposed systems and their components. Where a component is human, fallibility must be modelled by a stochastic agent. The identification of a model of decision-making over quantifiable options is investigated using the game-domain of Chess. Bayesian methods are used to infer the distribution of players’ skill levels from the moves they play rather than from their competitive results. The approach is used on large sets of games by players across a broad FIDE Elo range, and is in principle applicable to any scenario where high-value decisions are being made under pressure.

Modelling heterotachy in phylogenetic inference by reversible-jump Markov chain Monte Carlo

The rate at which a given site in a gene sequence alignment evolves over time may vary. This phenomenon-known as heterotachy-can bias or distort phylogenetic trees inferred from models of sequence evolution that assume rates of evolution are constant. Here, we describe a phylogenetic mixture model designed to accommodate heterotachy. The method sums the likelihood of the data at each site over more than one set of branch lengths on the same tree topology. A branch-length set that is best for one site may differ from the branch-length set that is best for some other site, thereby allowing different sites to have different rates of change throughout the tree. Because rate variation may not be present in all branches, we use a reversible-jump Markov chain Monte Carlo algorithm to identify those branches in which reliable amounts of heterotachy occur. We implement the method in combination with our 'pattern-heterogeneity' mixture model, applying it to simulated data and five published datasets. We find that complex evolutionary signals of heterotachy are routinely present over and above variation in the rate or pattern of evolution across sites, that the reversible-jump method requires far fewer parameters than conventional mixture models to describe it, and serves to identify the regions of the tree in which heterotachy is most pronounced. The reversible-jump procedure also removes the need for a posteriori tests of 'significance' such as the Akaike or Bayesian information criterion tests, or Bayes factors. Heterotachy has important consequences for the correct reconstruction of phylogenies as well as for tests of hypotheses that rely on accurate branch-length information. These include molecular clocks, analyses of tempo and mode of evolution, comparative studies and ancestral state reconstruction. The model is available from the authors' website, and can be used for the analysis of both nucleotide and morphological data.

A note on the accuracy of PAC-likelihood inference with microsatellite data

Stephens and Donnelly have introduced a simple yet powerful importance sampling scheme for computing the likelihood in population genetic models. Fundamental to the method is an approximation to the conditional probability of the allelic type of an additional gene, given those currently in the sample. As noted by Li and Stephens, the product of these conditional probabilities for a sequence of draws that gives the frequency of allelic types in a sample is an approximation to the likelihood, and can be used directly in inference. The aim of this note is to demonstrate the high level of accuracy of "product of approximate conditionals" (PAC) likelihood when used with microsatellite data. Results obtained on simulated microsatellite data show that this strategy leads to a negligible bias over a wide range of the scaled mutation parameter theta. Furthermore, the sampling variance of likelihood estimates as well as the computation time are lower than that obtained with importance sampling on the whole range of theta. It follows that this approach represents an efficient substitute to IS algorithms in computer intensive (e.g. MCMC) inference methods in population genetics. (c) 2006 Elsevier Inc. All rights reserved.

The automation and evaluation of nested clade phylogeographic analysis

Nested clade phylogeographic analysis (NCPA) is a popular method for reconstructing the demographic history of spatially distributed populations from genetic data. Although some parts of the analysis are automated, there is no unique and widely followed algorithm for doing this in its entirety, beginning with the data, and ending with the inferences drawn from the data. This article describes a method that automates NCPA, thereby providing a framework for replicating analyses in an objective way. To do so, a number of decisions need to be made so that the automated implementation is representative of previous analyses. We review how the NCPA procedure has evolved since its inception and conclude that there is scope for some variability in the manual application of NCPA. We apply the automated software to three published datasets previously analyzed manually and replicate many details of the manual analyses, suggesting that the current algorithm is representative of how a typical user will perform NCPA. We simulate a large number of replicate datasets for geographically distributed, but entirely random-mating, populations. These are then analyzed using the automated NCPA algorithm. Results indicate that NCPA tends to give a high frequency of false positives. In our simulations we observe that 14% of the clades give a conclusive inference that a demographic event has occurred, and that 75% of the datasets have at least one clade that gives such an inference. This is mainly due to the generation of multiple statistics per clade, of which only one is required to be significant to apply the inference key. We survey the inferences that have been made in recent publications and show that the most commonly inferred processes (restricted gene flow with isolation by distance and contiguous range expansion) are those that are commonly inferred in our simulations. However, published datasets typically yield a richer set of inferences with NCPA than obtained in our random-mating simulations, and further testing of NCPA with models of structured populations is necessary to examine its accuracy.

Inference on microsatellite mutation processes in the invasive mite, Varroa destructor, using reversible jump Markov chain Monte Carlo

Varroa destructor is a parasitic mite of the Eastern honeybee Apis cerana. Fifty years ago, two distinct evolutionary lineages (Korean and Japanese) invaded the Western honeybee Apis mellifera. This haplo-diploid parasite species reproduces mainly through brother sister matings, a system which largely favors the fixation of new mutations. In a worldwide sample of 225 individuals from 21 locations collected on Western honeybees and analyzed at 19 microsatellite loci, a series of de novo mutations was observed. Using historical data concerning the invasion, this original biological system has been exploited to compare three mutation models with allele size constraints for microsatellite markers: stepwise (SMM) and generalized (GSM) mutation models, and a model with mutation rate increasing exponentially with microsatellite length (ESM). Posterior probabilities of the three models have been estimated for each locus individually using reversible jump Markov Chain Monte Carlo. The relative support of each model varies widely among loci, but the GSM is the only model that always receives at least 9% support, whatever the locus. The analysis also provides robust estimates of mutation parameters for each locus and of the divergence time of the two invasive lineages (67,000 generations with a 90% credibility interval of 35,000-174,000). With an average of 10 generations per year, this divergence time fits with the last post-glacial Korea Japan land separation. (c) 2005 Elsevier Inc. All rights reserved.

Efficient modeling and inference for event-related fMRI data

Event-related functional magnetic resonance imaging (efMRI) has emerged as a powerful technique for detecting brains' responses to presented stimuli. A primary goal in efMRI data analysis is to estimate the Hemodynamic Response Function (HRF) and to locate activated regions in human brains when specific tasks are performed. This paper develops new methodologies that are important improvements not only to parametric but also to nonparametric estimation and hypothesis testing of the HRF. First, an effective and computationally fast scheme for estimating the error covariance matrix for efMRI is proposed. Second, methodologies for estimation and hypothesis testing of the HRF are developed. Simulations support the effectiveness of our proposed methods. When applied to an efMRI dataset from an emotional control study, our method reveals more meaningful findings than the popular methods offered by AFNI and FSL. (C) 2008 Elsevier B.V. All rights reserved.