Regularity of plurisubharmonic upper envelopes in big cohomology classes

The goal of this work is to prove the regularity of certain quasi-plurisubharmonic upper envelopes. Such envelopes appear in a natural way in the construction of hermitian metrics with minimal singularities on a big line bundle over a compact complex manifold. We prove that the complex Hessian forms of these envelopes are locally bounded outside an analytic set of singularities. It is furthermore shown that a parametrized version of this result yields a priori inequalities for the solution of the Dirichlet problem for a degenerate Monge-Ampere operator; applications to geodesics in the space of Kahler metrics are discussed. A similar technique provides a logarithmic modulus of continuity for Tsuji's "supercanonical" metrics, which generalize a well-known construction of Narasimhan-Simha.Comment: 27 pages, no figure

Computational Dynamic Market Risk Measures in Discrete Time Setting

Different approaches to defining dynamic market risk measures are available in the literature. Most are focused or derived from probability theory, economic behavior or dynamic programming. Here, we propose an approach to define and implement dynamic market risk measures based on recursion and state economy representation. The proposed approach is to be implementable and to inherit properties from static market risk measures.Comment: 16 pages, 3 figure

Efficient learning in ABC algorithms

Approximate Bayesian Computation has been successfully used in population genetics to bypass the calculation of the likelihood. These methods provide accurate estimates of the posterior distribution by comparing the observed dataset to a sample of datasets simulated from the model. Although parallelization is easily achieved, computation times for ensuring a suitable approximation quality of the posterior distribution are still high. To alleviate the computational burden, we propose an adaptive, sequential algorithm that runs faster than other ABC algorithms but maintains accuracy of the approximation. This proposal relies on the sequential Monte Carlo sampler of Del Moral et al. (2012) but is calibrated to reduce the number of simulations from the model. The paper concludes with numerical experiments on a toy example and on a population genetic study of Apis mellifera, where our algorithm was shown to be faster than traditional ABC schemes

Conductance increases produced by bath application of cholinergic agonists to Electrophorus electroplaques

When solutions containing agonists are applied to the innervated face of an Electrophorus electroplaque, the membrane's conductance increases. The agonist-induced conductance is increased at more negative membrane potentials. The "instantaneous" current-voltage curve for agonist-induced currents is linear and shows a reversal potential near zero mV; chord conductances, calculated on the basis of this reversal potential, change epsilon-fold for every 62-mV change in potential when the conductance is small. Conductance depends non- linearly on small agonist concentrations; at all potentials, the dose-response curve has a Hill coefficient of 1.45 for decamethonium (Deca) and 1.90 for carbamylcholine (Carb). With agonist concentrations greater than 10^(-4) M Carb or 10^(-5) M Deca, the conductance rises to a peak 0.5-1.5 min after introduction of agonist, then declines with time; this effect resembles the "desensitization" reported for myoneural junctions. Elapid alpha-toxin, tubocurarine, and desensitization reduce the conductance without changing the effects of potential; the apparent dissociation constant for tubocurarine is 2 X 10^(-7) M. By contrast, procaine effects a greater fractional inhibition of the conductance at high negative potentials

Approximate Bayesian Computational methods

Also known as likelihood-free methods, approximate Bayesian computational (ABC) methods have appeared in the past ten years as the most satisfactory approach to untractable likelihood problems, first in genetics then in a broader spectrum of applications. However, these methods suffer to some degree from calibration difficulties that make them rather volatile in their implementation and thus render them suspicious to the users of more traditional Monte Carlo methods. In this survey, we study the various improvements and extensions made to the original ABC algorithm over the recent years.Comment: 7 figure

Public service guarantees:Exploring the design and implementation of service guarantees in public settings

Zowel private als publieke organisaties gebruiken servicegaranties om de klantgerichteid en -tevredenheid te vergroten. Servicegaranties zijn expliciet gecommuniceerde beloften naar (potentiële) klanten om specifieke servicelevels of zelfs om klanttevredenheid te realiseren. Over het algemeen is hier een compensatie voor klanten aan verbonden voor het geval de organisatie haar belofte(n) niet waarmaakt. Dit proefschrift gebruikt kwalitatief onderzoek en experimentele vignette studies gericht op het ontwerp en de implementatie van servicegaranties in de publieke dienstverlening. Onderzoek naar het ontwerp laat zien dat een servicegarantie concrete beloften over de belangrijkste dienstverleningsaspecten voor klanten dient te bevatten. Het expliciet beloven van een compensatie draagt bij aan het positieve imago van de organisatie. Compensatie bieden na een fout leidt tot een hogere klanttevredenheid. Dit kan een financiële compensatie zijn. Maar het kan ook een psychologische vorm zijn zoals pro-sociale compensatie waarbij de organisatie het bedrag namens de klant aan een goed doel doneert. Pro-sociale compensatie heeft positieve effecten op het maatschappelijk verantwoord ondernemen imago van de organisatie en de tevredenheid van klanten. Onderzoek naar de implementatie van servicegaranties in een organisatie en een netwerk van serviceorganisaties heeft geresulteerd in twee modellen met organisatorische randvoorwaarden. Deze tonen dat commitment van het topmanagement, het verbinden van de servicegarantie aan de strategie, actieve betrokkenheid van medewerkers en deze beslisruimte geven om conform de inhoud van de servicegarantie te werken, actieve betrokkenheid van klanten en continu reflecteren, leren & verbeteren belangrijke clusters zijn. Voor een netwerk zijn daarnaast de onderlinge verstandhouding en samenwerking in de keten belangrijk

Reliable ABC model choice via random forests

Approximate Bayesian computation (ABC) methods provide an elaborate approach to Bayesian inference on complex models, including model choice. Both theoretical arguments and simulation experiments indicate, however, that model posterior probabilities may be poorly evaluated by standard ABC techniques. We propose a novel approach based on a machine learning tool named random forests to conduct selection among the highly complex models covered by ABC algorithms. We thus modify the way Bayesian model selection is both understood and operated, in that we rephrase the inferential goal as a classification problem, first predicting the model that best fits the data with random forests and postponing the approximation of the posterior probability of the predicted MAP for a second stage also relying on random forests. Compared with earlier implementations of ABC model choice, the ABC random forest approach offers several potential improvements: (i) it often has a larger discriminative power among the competing models, (ii) it is more robust against the number and choice of statistics summarizing the data, (iii) the computing effort is drastically reduced (with a gain in computation efficiency of at least fifty), and (iv) it includes an approximation of the posterior probability of the selected model. The call to random forests will undoubtedly extend the range of size of datasets and complexity of models that ABC can handle. We illustrate the power of this novel methodology by analyzing controlled experiments as well as genuine population genetics datasets. The proposed methodologies are implemented in the R package abcrf available on the CRAN.Comment: 39 pages, 15 figures, 6 table