A transit timing analysis of seven RISE light curves of the exoplanet system HAT-P-3

We present seven light curves of the exoplanet system HAT-P-3, taken as part of a transit timing programme using the rapid imager to search for exoplanets instrument on the Liverpool Telescope. The light curves are analysed using a Markov chain Monte Carlo algorithm to update the parameters of the system. The inclination is found to be i = 86.75+0.22-0.21��, the planet-star radius ratio to be Rp/R* = 0.1098+0.0010-0.0012 and the stellar radius to be R* = 0.834+0.018-0.026Rsolar, consistent with previous results but with a significant improvement in the precision. Central transit times and uncertainties for each light curve are also determined, and a residual permutation algorithm is used as an independent check on the errors. The transit times are found to be consistent with a linear ephemeris, and a new ephemeris is calculated as Tc(0) = 2454856.70118 +/- 0.00018 HJD and P = 2.899738 +/- 0.000007 d. Model timing residuals are fitted to the measured timing residuals to place upper mass limits for a hypothetical perturbing planet as a function of the period ratio. These show that we have probed for planets with masses as low as 0.33 and 1.81 M? in the interior and exterior 2:1 resonances, respectively, assuming the planets are initially in circular orbits.

Ground-based detection of thermal emission from the exoplanet WASP-19b

We present an occultation of the newly discovered hot Jupiter system WASP-19, observed with the High Acuity Wide-field K-band Imager instrument on the VLT, in order to measure thermal emission from the planet's dayside at ~2��m. The light curve was analysed using a Markov Chain Monte Carlo method to find the eclipse depth and the central transit time. The transit depth was found to be 0.366 +/- 0.072 per cent, corresponding to a brightness temperature of 2540 +/- 180 K. This is significantly higher than the calculated (zero-albedo) equilibrium temperature and indicates that the planet shows poor redistribution of heat to the night side, consistent with models of highly irradiated planets. Further observations are needed to confirm the existence of a temperature inversion and possibly molecular emission lines. The central eclipse time was found to be consistent with a circular orbit.

A lower mass for the exoplanet WASP-21b

We present high-precision transit observations of the exoplanet WASP-21b, obtained with the Rapid Imager to Search for Exoplanets instrument mounted on the 2.0-m Liverpool Telescope. A transit model is fitted, coupled with a Markov chain Monte Carlo routine, to derive accurate system parameters. The two new high-precision transits allow us to estimate the stellar density directly from the light curve. Our analysis suggests that WASP-21 is evolving off the main sequence which led to a previous overestimation of the stellar density. Using isochrone interpolation, we find a stellar mass of 0.86 �� 0.04 Msun, which is significantly lower than previously reported (1.01 �� 0.03 Msun). Consequently, we find a lower planetary mass of 0.27 �� 0.01 MJup. A lower inclination (87?4 �� 0?3) is also found for the system than previously reported, resulting in a slightly larger stellar (R*= 1.10 �� 0.03 Rsun) and planetary radius (Rp= 1.14 �� 0.04 RJup). The planet radius suggests a hydrogen/helium composition with no core which strengthens the correlation between planetary density and host star metallicity. A new ephemeris is determined for the system, i.e. T0= 245 5084.519 74 �� 0.000 20 (HJD) and P= 4.322 5060 �� 0.000 0031 d. We found no transit timing variations in WASP-21b.

High-precision transit observations of the exoplanet WASP-13b with the RISE instrument

WASP-13b is a sub-Jupiter mass exoplanet orbiting a G1V type star with a period of 4.35 d.The current uncertainty in its impact parameter (0 < b < 0.46) results in poorly definedstellar and planetary radii. To better constrain the impact parameter, we have obtained highprecisiontransit observations with the rapid imager to search for exoplanets (RISE) instrumentmounted on 2.0-m Liverpool Telescope. We present four new transits which are fitted witha Markov chain Monte Carlo routine to derive accurate system parameters. We found anorbital inclination of 85. ��� 2 �� 0. ��� 3 resulting in stellar and planetary radii of 1.56 �� 0.04 Rand 1.39 �� 0.05RJup, respectively. This suggests that the host star has evolved off the mainsequence and is in the hydrogen-shell-burning phase.We also discuss how the limb darkeningaffects the derived system parameters.With a density of 0.17��J,WASP-13b joins the group oflow-density planets whose radii are too large to be explained by standard irradiation models.We derive a new ephemeris for the system, T0 = 245 5575.5136 �� 0.0016 (HJD) and P =4.353 011 �� 0.000 013 d. The planet equilibrium temperature (Tequ = 1500 K) and the brighthost star (V = 10.4mag) make it a good candidate for follow-up atmospheric studies.

Evaluation of genetic substructure in the Irish Study of High-Density Schizophrenia Families

The presence of genetic substructure has the potential to diminish the chances of detecting a linkage signal. Using a Markov chain Monte Carlo procedure developed by Pritchard and colleagues and implemented in the program STRUCTURE, we evaluated the evidence for genetic substructure using genotypes from 37 microsatellite markers in affected individuals selected at random from 263 multiplex families in the Irish Study of High-Density Schizophrenia Families. We found no evidence for the presence of genetic substructure in this sample.

Characterization of the Kepler-101 planetary system with HARPS-N. A hot super-Neptune with an Earth-sized low-mass companion

We characterize the planetary system Kepler-101 by performing a combined differential evolution Markov chain Monte Carlo analysisof Kepler data and forty radial velocities obtained with the HARPS-N spectrograph. This system was previously validated and iscomposed of a hot super-Neptune, Kepler-101b, and an Earth-sized planet, Kepler-101c. These two planets orbit the slightly evolvedand metal-rich G-type star in 3.49 and 6.03 days, respectively. With mass Mp = 51.1+5.1���4.7 M���, radius Rp = 5.77+0.85���0.79 R���, and density ��p = 1.45+0.83 ���0.48 g cm���3, Kepler-101b is the first fully characterized super-Neptune, and its density suggests that heavy elements makeup a significant fraction of its interior; more than 60% of its total mass. Kepler-101c has a radius of 1.25+0.19���0.17 R���, which implies theabsence of any H/He envelope, but its mass could not be determined because of the relative faintness of the parent star for highly precise radial-velocity measurements (Kp = 13.8) and the limited number of radial velocities. The 1�� upper limit, Mp < 3.8 M���, excludes a pure iron composition with a probability of 68.3%. The architecture of the planetary system Kepler-101 ��� containing aclose-in giant planet and an outer Earth-sized planet with a period ratio slightly larger than the 3:2 resonance ��� is certainly of interest for scenarios of planet formation and evolution. This system does not follow the previously reported trend that the larger planet has the longer period in the majority of Kepler systems of planet pairs with at least one Neptune-sized or larger planet.

Improving sampling, optimization and feature extraction in Boltzmann machines

L���apprentissage supervis�� de r��seaux hi��rarchiques �� grande ��chelle conna��t pr��sentement un succ��s fulgurant. Malgr�� cette effervescence, l���apprentissage non-supervis�� repr��sente toujours, selon plusieurs chercheurs, un ��l��ment cl�� de l���Intelligence Artificielle, o�� les agents doivent apprendre �� partir d���un nombre potentiellement limit�� de donn��es. Cette th��se s���inscrit dans cette pens��e et aborde divers sujets de recherche li��s au probl��me d���estimation de densit�� par l���entremise des machines de Boltzmann (BM), mod��les graphiques probabilistes au coeur de l���apprentissage profond. Nos contributions touchent les domaines de l�����chantillonnage, l���estimation de fonctions de partition, l���optimisation ainsi que l���apprentissage de repr��sentations invariantes. Cette th��se d��bute par l���exposition d���un nouvel algorithme d'��chantillonnage adaptatif, qui ajuste (de fa ��con automatique) la temp��rature des cha��nes de Markov sous simulation, afin de maintenir une vitesse de convergence ��lev��e tout au long de l���apprentissage. Lorsqu���utilis�� dans le contexte de l���apprentissage par maximum de vraisemblance stochastique (SML), notre algorithme engendre une robustesse accrue face �� la s��lection du taux d���apprentissage, ainsi qu���une meilleure vitesse de convergence. Nos r��sultats sont pr��sent ��es dans le domaine des BMs, mais la m��thode est g��n��rale et applicable �� l���apprentissage de tout mod��le probabiliste exploitant l�����chantillonnage par cha��nes de Markov. Tandis que le gradient du maximum de vraisemblance peut-��tre approxim�� par ��chantillonnage, l�����valuation de la log-vraisemblance n��cessite un estim�� de la fonction de partition. Contrairement aux approches traditionnelles qui consid��rent un mod��le donn�� comme une bo��te noire, nous proposons plut��t d���exploiter la dynamique de l���apprentissage en estimant les changements successifs de log-partition encourus �� chaque mise �� jour des param��tres. Le probl��me d���estimation est reformul�� comme un probl��me d���inf��rence similaire au filtre de Kalman, mais sur un graphe bi-dimensionnel, o�� les dimensions correspondent aux axes du temps et au param��tre de temp��rature. Sur le th��me de l���optimisation, nous pr��sentons ��galement un algorithme permettant d���appliquer, de mani��re efficace, le gradient naturel �� des machines de Boltzmann comportant des milliers d���unit��s. Jusqu����� pr��sent, son adoption ��tait limit��e par son haut co��t computationel ainsi que sa demande en m��moire. Notre algorithme, Metric-Free Natural Gradient (MFNG), permet d�����viter le calcul explicite de la matrice d���information de Fisher (et son inverse) en exploitant un solveur lin��aire combin�� �� un produit matrice-vecteur efficace. L���algorithme est prometteur: en terme du nombre d�����valuations de fonctions, MFNG converge plus rapidement que SML. Son impl��mentation demeure malheureusement inefficace en temps de calcul. Ces travaux explorent ��galement les m��canismes sous-jacents �� l���apprentissage de repr��sentations invariantes. �� cette fin, nous utilisons la famille de machines de Boltzmann restreintes ���spike & slab��� (ssRBM), que nous modifions afin de pouvoir mod��liser des distributions binaires et parcimonieuses. Les variables latentes binaires de la ssRBM peuvent ��tre rendues invariantes �� un sous-espace vectoriel, en associant �� chacune d���elles, un vecteur de variables latentes continues (d��nomm��es ���slabs���). Ceci se traduit par une invariance accrue au niveau de la repr��sentation et un meilleur taux de classification lorsque peu de donn��es ��tiquet��es sont disponibles. Nous terminons cette th��se sur un sujet ambitieux: l���apprentissage de repr��sentations pouvant s��parer les facteurs de variations pr��sents dans le signal d���entr��e. Nous proposons une solution �� base de ssRBM bilin��aire (avec deux groupes de facteurs latents) et formulons le probl��me comme l���un de ���pooling��� dans des sous-espaces vectoriels compl��mentaires.

Evolutionary history of vegetative reproduction in Porpidia s. l. (lichen-forming ascomycota)

The evolutionary history of gains and losses of vegetative reproductive propagules (soredia) in Porpidia s.l., a group of lichen-forming ascomycetes, was clarified using Bayesian Markov chain Monte Carlo (MCMC) approaches to monophyly tests and a combined MCMC and maximum likelihood approach to ancestral character state reconstructions. The MCMC framework provided confidence estimates for the reconstructions of relationships and ancestral character states, which formed the basis for tests of evolutionary hypotheses. Monophyly tests rejected all hypotheses that predicted any clustering of reproductive modes in extant taxa. In addition, a nearest-neighbor statistic could not reject the hypothesis that the vegetative reproductive mode is randomly distributed throughout the group. These results show that transitions between presence and absence of the vegetative reproductive mode within Porpidia s.l. occurred several times and independently of each other. Likelihood reconstructions of ancestral character states at selected nodes suggest that - contrary to previous thought - the ancestor to Porpidia s.l. already possessed the vegetative reproductive mode. Furthermore, transition rates are reconstructed asymmetrically with the vegetative reproductive mode being gained at a much lower rate than it is lost. A cautious note has to be added, because a simulation study showed that the ancestral character state reconstructions were highly dependent on taxon sampling. However, our central conclusions, particularly the higher rate of change from vegetative reproductive mode present to absent than vice versa within Porpidia s.l., were found to be broadly independent of taxon sampling. [Ancestral character state reconstructions; Ascomycota, Bayesian inference; hypothesis testing; likelihood; MCMC; Porpidia; reproductive systems]

Evolution of micromorphological and chemical characters in the lichen-forming fungal family Pertusariaceae

Micromorphological characters of the fruiting bodies, such as ascus-type and hymenial amyloidity, and secondary chemistry have been widely employed as key characters in Ascomycota classification. However, the evolution of these characters has yet not been studied using molecular phylogenies. We have used a combined Bayesian and maximum likelihood based approach to trace character evolution on a tree inferred from a combined analysis of nuclear and mitochondrial ribosomal DNA sequences. The maximum likelihood aspect overcomes simplifications inherent in maximum parsimony methods, whereas the Markov chain Monte Carlo aspect renders results independent of any particular phylogenetic tree. The results indicate that the evolution of the two chemical characters is quite different, being stable once developed for the medullary lecanoric acid, whereas the cortical chlorinated xanthones appear to have been lost several times. The current ascus-types and the amyloidity of the hymenial gel in Pertusariaceae appear to have been developed within the family. The basal ascus-type of pertusarialean fungi remains unknown. (c) 2006 The Linnean Society of London, Biological Journal of the Linnean Society, 2006, 89, 615-626.

Linkage disequilibrium assessment via log-linear modeling of SNP haplotype frequencies

Analyses of high-density single-nucleotide polymorphism (SNP) data, such as genetic mapping and linkage disequilibrium (LD) studies, require phase-known haplotypes to allow for the correlation between tightly linked loci. However, current SNP genotyping technology cannot determine phase, which must be inferred statistically. In this paper, we present a new Bayesian Markov chain Monte Carlo (MCMC) algorithm for population haplotype frequency estimation, particulary in the context of LD assessment. The novel feature of the method is the incorporation of a log-linear prior model for population haplotype frequencies. We present simulations to suggest that 1) the log-linear prior model is more appropriate than the standard coalescent process in the presence of recombination (>0.02cM between adjacent loci), and 2) there is substantial inflation in measures of LD obtained by a "two-stage" approach to the analysis by treating the "best" haplotype configuration as correct, without regard to uncertainty in the recombination process. Genet Epidemiol 25:106-114, 2003. (C) 2003 Wiley-Liss, Inc.

Measuring gametic disequilibrium from multilocus data

We describe a Bayesian approach to analyzing multilocus genotype or haplotype data to assess departures from gametic (linkage) equilibrium. Our approach employs a Markov chain Monte Carlo (MCMC) algorithm to approximate the posterior probability distributions of disequilibrium parameters. The distributions are computed exactly in some simple settings. Among other advantages, posterior distributions can be presented visually, which allows the uncertainties in parameter estimates to be readily assessed. In addition, background knowledge can be incorporated, where available, to improve the precision of inferences. The method is illustrated by application to previously published datasets; implications for multilocus forensic match probabilities and for simple association-based gene mapping are also discussed.

Bayesian parameter estimation for latent Markov random fields and social networks

Undirected graphical models are widely used in statistics, physics and machine vision. However Bayesian parameter estimation for undirected models is extremely challenging, since evaluation of the posterior typically involves the calculation of an intractable normalising constant. This problem has received much attention, but very little of this has focussed on the important practical case where the data consists of noisy or incomplete observations of the underlying hidden structure. This paper specifically addresses this problem, comparing two alternative methodologies. In the first of these approaches particle Markov chain Monte Carlo (Andrieu et al., 2010) is used to efficiently explore the parameter space, combined with the exchange algorithm (Murray et al., 2006) for avoiding the calculation of the intractable normalising constant (a proof showing that this combination targets the correct distribution in found in a supplementary appendix online). This approach is compared with approximate Bayesian computation (Pritchard et al., 1999). Applications to estimating the parameters of Ising models and exponential random graphs from noisy data are presented. Each algorithm used in the paper targets an approximation to the true posterior due to the use of MCMC to simulate from the latent graphical model, in lieu of being able to do this exactly in general. The supplementary appendix also describes the nature of the resulting approximation.

A generalised Bayesian instrumental variable approach under student t-distributed errors with application

Bayesian analysis is given of an instrumental variable model that allows for heteroscedasticity in both the structural equation and the instrument equation. Specifically, the approach for dealing with heteroscedastic errors in Geweke (1993) is extended to the Bayesian instrumental variable estimator outlined in Rossi et al. (2005). Heteroscedasticity is treated by modelling the variance for each error using a hierarchical prior that is Gamma distributed. The computation is carried out by using a Markov chain Monte Carlo sampling algorithm with an augmented draw for the heteroscedastic case. An example using real data illustrates the approach and shows that ignoring heteroscedasticity in the instrument equation when it exists may lead to biased estimates.

Methane in the atmosphere of the transiting hot Neptune GJ436b?

We present an analysis of seven primary transit observations of the hot Neptune GJ436b at 3.6, 4.5, and 8 ��m obtained with the Infrared Array Camera on the Spitzer Space Telescope. After correcting for systematic effects, we fitted the light curves using the Markov Chain Monte Carlo technique. Combining these new data with the EPOXI, Hubble Space Telescope, and ground-based V, I, H, and Ks published observations, the range 0.5-10 ��m can be covered. Due to the low level of activity of GJ436, the effect of starspots on the combination of transits at different epochs is negligible at the accuracy of the data set. Representative climate models were calculated by using a three-dimensional, pseudospectral general circulation model with idealized thermal forcing. Simulated transit spectra of GJ436b were generated using line-by-line radiative transfer models including the opacities of the molecular species expected to be present in such a planetary atmosphere. A new, ab-initio-calculated, line list for hot ammonia has been used for the first time. The photometric data observed at multiple wavelengths can be interpreted with methane being the dominant absorption after molecular hydrogen, possibly with minor contributions from ammonia, water, and other molecules. No clear evidence of carbon monoxide and carbon dioxide is found from transit photometry. We discuss this result in the light of a recent paper where photochemical disequilibrium is hypothesized to interpret secondary transit photometric data. We show that the emission photometric data are not incompatible with the presence of abundant methane, but further spectroscopic data are desirable to confirm this scenario.

Bayesian nonlinear regression models with scale mixtures of skew-normal distributions: Estimation and case influence diagnostics

The purpose of this paper is to develop a Bayesian analysis for nonlinear regression models under scale mixtures of skew-normal distributions. This novel class of models provides a useful generalization of the symmetrical nonlinear regression models since the error distributions cover both skewness and heavy-tailed distributions such as the skew-t, skew-slash and the skew-contaminated normal distributions. The main advantage of these class of distributions is that they have a nice hierarchical representation that allows the implementation of Markov chain Monte Carlo (MCMC) methods to simulate samples from the joint posterior distribution. In order to examine the robust aspects of this flexible class, against outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. Further, some discussions on the model selection criteria are given. The newly developed procedures are illustrated considering two simulations study, and a real data previously analyzed under normal and skew-normal nonlinear regression models. (C) 2010 Elsevier B.V. All rights reserved.