Bayesian optimization using sequential Monte Carlo

We consider the problem of optimizing a real-valued continuous function $f$ using a Bayesian approach, where the evaluations of $f$ are chosen sequentially by combining prior information about $f$, which is described by a random process model, and past evaluation results. The main difficulty with this approach is to be able to compute the posterior distributions of quantities of interest which are used to choose evaluation points. In this article, we decide to use a Sequential Monte Carlo (SMC) approach

Surtsey and Mount St. Helens: a comparison of early succession rates

Surtsey and Mount St. Helens are celebrated but very different volcanoes. Permanent plots allow for comparisons that reveal mechanisms that control succession and its rate and suggest general principles. We estimated rates from structure development, species composition using detrended correspondence analysis (DCA), changes in Euclidean distance (ED) of DCA vectors, and by principal components analysis (PCA) of DCA. On Surtsey, rates determined from DCA trajectory analyses decreased as follows: gull colony on lava with sand > gull colony on lava, no sand ≫ lava with sand > sand spit > block lava > tephra. On Mount St. Helens, plots on lahar deposits near woodlands were best developed. The succession rates of open meadows declined as follows: <i>Lupinus</i>-dominated pumice > protected ridge with <i>Lupinus</i> > other pumice and blasted sites > isolated lahar meadows > barren plain. Despite the prominent contrasts between the volcanoes, we found several common themes. Isolation restricted the number of colonists on Surtsey and to a lesser degree on Mount St. Helens. Nutrient input from outside the system was crucial. On Surtsey, seabirds fashioned very fertile substrates, while on Mount St. Helens wind brought a sparse nutrient rain, then <i>Lupinus</i> enhanced fertility to promote succession. Environmental stress limits succession in both cases. On Surtsey, bare lava, compacted tephra and infertile sands restrict development. On Mount St. Helens, exposure to wind and infertility slow succession

On the Stability and the Approximation of Branching Distribution Flows, with Applications to Nonlinear Multiple Target Filtering

We analyse the exponential stability properties of a class of measure-valued equations arising in nonlinear multi-target filtering problems. We also prove the uniform convergence properties w.r.t. the time parameter of a rather general class of stochastic filtering algorithms, including sequential Monte Carlo type models and mean eld particle interpretation models. We illustrate these results in the context of the Bernoulli and the Probability Hypothesis Density filter, yielding what seems to be the first results of this kind in this subject

On particle Gibbs Markov chain Monte Carlo models

This article analyses a new class of advanced particle Markov chain Monte Carlo algorithms recently introduced by Andrieu, Doucet, and Holenstein (2010). We present a natural interpretation of these methods in terms of well known unbiasedness properties of Feynman-Kac particle measures, and a new duality with many-body Feynman-Kac models. This perspective sheds a new light on the foundations and the mathematical analysis of this class of methods. A key consequence is the equivalence between the backward and ancestral particle Markov chain Monte Carlo methods, and Gibbs sampling of a many-body Feynman-Kac target distribution. Our approach also presents a new stochastic differential calculus based on geometric combinatorial techniques to derive explicit non-asymptotic Taylor type series of the semigroup of a class of particle Markov chain Monte Carlo models around their invariant measures with respect to the population size of the auxiliary particle sampler. These results provide sharp quan- titative estimates of the convergence properties of conditional particle Markov chain models with respect to the time horizon and the size of the systems. We illustrate the implication of these results with sharp estimates of the contraction coefficient and the Lyapunov exponent of conditional particle samplers, and explicit and non-asymptotic Lp-mean error decompositions of the law of the random states around the limiting invariant measure. The abstract framework developed in the article also allows the design of natural extensions to island (also called SMC2) type particle methodologies

La forma farmacéutica un factor a valorar en el SFT

Mujer de 80 años polimedicada, que para mejorar la adherencia a su tratamiento farmacológico participa del servicio de SPD (Sistema Personalizado de Dosificación) de la farmacia. A todos los usuarios del servicio de SPD se les incluye en el servicio de Seguimiento Farmacoterapéutico (SFT). Diagnosticada de hipertensión arterial, dislipemia, artrosis,obesidad, dolor, insuficiencia cardiaca secundaria a cardiopatía hipertensiva moderada-severa con FEVI deprimida moderada con bloqueo completo de rama izquierda, diabetes mellitus insulino-dependiente, y fibrilación auricular; anticoagulada con acenocumarol 4 mg (sintrom 4 mg ®). En Noviembre de 2013, se diagnostica de novo de Insuficiencia Renal Crónica III con hiperparatiroidismo secundario

La forma farmacéutica un factor a valorar en el SFT

Mujer de 80 años polimedicada, que para mejorar la adherencia a su tratamiento farmacológico participa del servicio de SPD (Sistema Personalizado de Dosificación) de la farmacia. A todos los usuarios del servicio de SPD se les incluye en el servicio de Seguimiento Farmacoterapéutico (SFT). Diagnosticada de hipertensión arterial, dislipemia, artrosis,obesidad, dolor, insuficiencia cardiaca secundaria a cardiopatía hipertensiva moderada-severa con FEVI deprimida moderada con bloqueo completo de rama izquierda, diabetes mellitus insulino-dependiente, y fibrilación auricular; anticoagulada con acenocumarol 4 mg (sintrom 4 mg ®). En Noviembre de 2013, se diagnostica de novo de Insuficiencia Renal Crónica III con hiperparatiroidismo secundario

Duroc and Iberian Pork Neural Network Classification by Visible and Near Infrared Reflectance Spectroscopy

a b s t r a c t Visible and near infrared reflectance spectroscopy (VIS/NIRS) was used to differentiate between Duroc and Iberian pork in the M. masseter. Samples of Duroc (n = 15) and Iberian (n = 15) pig muscles were scanned in the VIS/NIR region (350-2500 nm) using a portable spectral radiometer. Both mutual information and VIS/NIRS spectra characterization were developed to generate a ranking of variables and the data were then processed by artificial neural networks, establishing 1, 3, or 10 wavelengths as input variable for classifying between the pig breeds. The models correctly classified &gt;70% of all problem assumptions, with a correct classification of &gt;95% for the three-variable assumption using either mutual information ranking or VIS/NIRS spectra characterization. These results demonstrate the potential value of the VIS/ NIRS technique as an objective and rapid method for the authentication and identification of Duroc and Iberian pork

Critical properties of random anisotropy magnets

The problem of critical behaviour of three dimensional random anisotropy magnets, which constitute a wide class of disordered magnets is considered. Previous results obtained in experiments, by Monte Carlo simulations and within different theoretical approaches give evidence for a second order phase transition for anisotropic distributions of the local anisotropy axes, while for the case of isotropic distribution such transition is absent. This outcome is described by renormalization group in its field theoretical variant on the basis of the random anisotropy model. Considerable attention is paid to the investigation of the effective critical behaviour which explains the observation of different behaviour in the same universality class.Comment: 41 pages, 10 figure