4,970 research outputs found
Bayesian Methods for Analysis and Adaptive Scheduling of Exoplanet Observations
We describe work in progress by a collaboration of astronomers and
statisticians developing a suite of Bayesian data analysis tools for extrasolar
planet (exoplanet) detection, planetary orbit estimation, and adaptive
scheduling of observations. Our work addresses analysis of stellar reflex
motion data, where a planet is detected by observing the "wobble" of its host
star as it responds to the gravitational tug of the orbiting planet. Newtonian
mechanics specifies an analytical model for the resulting time series, but it
is strongly nonlinear, yielding complex, multimodal likelihood functions; it is
even more complex when multiple planets are present. The parameter spaces range
in size from few-dimensional to dozens of dimensions, depending on the number
of planets in the system, and the type of motion measured (line-of-sight
velocity, or position on the sky). Since orbits are periodic, Bayesian
generalizations of periodogram methods facilitate the analysis. This relies on
the model being linearly separable, enabling partial analytical
marginalization, reducing the dimension of the parameter space. Subsequent
analysis uses adaptive Markov chain Monte Carlo methods and adaptive importance
sampling to perform the integrals required for both inference (planet detection
and orbit measurement), and information-maximizing sequential design (for
adaptive scheduling of observations). We present an overview of our current
techniques and highlight directions being explored by ongoing research.Comment: 29 pages, 11 figures. An abridged version is accepted for publication
in Statistical Methodology for a special issue on astrostatistics, with
selected (refereed) papers presented at the Astronomical Data Analysis
Conference (ADA VI) held in Monastir, Tunisia, in May 2010. Update corrects
equation (3
Fourier Analysis of Stochastic Sampling Strategies for Assessing Bias and Variance in Integration
Each pixel in a photorealistic, computer generated picture is calculated by approximately integrating all the light arriving at the pixel, from the virtual scene. A common strategy to calculate these high-dimensional integrals is to average the estimates at stochastically sampled locations. The strategy with which the sampled locations are chosen is of utmost importance in deciding the quality of the approximation, and hence rendered image.
We derive connections between the spectral properties of stochastic sampling patterns and the first and second order statistics of estimates of integration using the samples. Our equations provide insight into the assessment of stochastic sampling strategies for integration. We show that the amplitude of the expected Fourier spectrum of sampling patterns is a useful indicator of the bias when used in numerical integration. We deduce that estimator variance is directly dependent on the variance of the sampling spectrum over multiple realizations of the sampling pattern. We then analyse Gaussian jittered sampling, a simple variant of jittered sampling, that allows a smooth trade-off of bias for variance in uniform (regular grid) sampling. We verify our predictions using spectral measurement, quantitative integration experiments and qualitative comparisons of rendered images.</jats:p
Free energy reconstruction from steered dynamics without post-processing
Various methods achieving importance sampling in ensembles of nonequilibrium
trajectories enable to estimate free energy differences and, by
maximum-likelihood post-processing, to reconstruct free energy landscapes.
Here, based on Bayes theorem, we propose a more direct method in which a
posterior likelihood function is used both to construct the steered dynamics
and to infer the contribution to equilibrium of all the sampled states. The
method is implemented with two steering schedules. First, using non-autonomous
steering, we calculate the migration barrier of the vacancy in Fe-alpha.
Second, using an autonomous scheduling related to metadynamics and equivalent
to temperature-accelerated molecular dynamics, we accurately reconstruct the
two-dimensional free energy landscape of the 38-atom Lennard-Jones cluster as a
function of an orientational bond-order parameter and energy, down to the
solid-solid structural transition temperature of the cluster and without
maximum-likelihood post-processing.Comment: Accepted manuscript in Journal of Computational Physics, 7 figure
Computational Particle Physics for Event Generators and Data Analysis
High-energy physics data analysis relies heavily on the comparison between
experimental and simulated data as stressed lately by the Higgs search at LHC
and the recent identification of a Higgs-like new boson. The first link in the
full simulation chain is the event generation both for background and for
expected signals. Nowadays event generators are based on the automatic
computation of matrix element or amplitude for each process of interest.
Moreover, recent analysis techniques based on the matrix element likelihood
method assign probabilities for every event to belong to any of a given set of
possible processes. This method originally used for the top mass measurement,
although computing intensive, has shown its power at LHC to extract the new
boson signal from the background.
Serving both needs, the automatic calculation of matrix element is therefore
more than ever of prime importance for particle physics. Initiated in the
eighties, the techniques have matured for the lowest order calculations
(tree-level), but become complex and CPU time consuming when higher order
calculations involving loop diagrams are necessary like for QCD processes at
LHC. New calculation techniques for next-to-leading order (NLO) have surfaced
making possible the generation of processes with many final state particles (up
to 6). If NLO calculations are in many cases under control, although not yet
fully automatic, even higher precision calculations involving processes at
2-loops or more remain a big challenge.
After a short introduction to particle physics and to the related theoretical
framework, we will review some of the computing techniques that have been
developed to make these calculations automatic. The main available packages and
some of the most important applications for simulation and data analysis, in
particular at LHC will also be summarized.Comment: 19 pages, 11 figures, Proceedings of CCP (Conference on Computational
Physics) Oct. 2012, Osaka (Japan) in IOP Journal of Physics: Conference
Serie
Efficient Bayesian inference via Monte Carlo and machine learning algorithms
Mención Internacional en el título de doctorIn many fields of science and engineering, we are faced with an inverse problem where
we aim to recover an unobserved parameter or variable of interest from a set of observed
variables. Bayesian inference is a probabilistic approach for inferring this unknown parameter
that has become extremely popular, finding application in myriad problems in
fields such as machine learning, signal processing, remote sensing and astronomy. In
Bayesian inference, all the information about the parameter is summarized by the posterior
distribution. Unfortunately, the study of the posterior distribution requires the computation
of complicated integrals, that are analytically intractable and need to be approximated.
Monte Carlo is a huge family of sampling algorithms for performing optimization
and numerical integration that has become the main horsepower for carrying out Bayesian
inference. The main idea of Monte Carlo is that we can approximate the posterior distribution
by a set of samples, obtained by an iterative process that involves sampling from a
known distribution. Markov chain Monte Carlo (MCMC) and importance sampling (IS)
are two important groups of Monte Carlo algorithms. This thesis focuses on developing
and analyzing Monte Carlo algorithms (either MCMC, IS or combination of both)
under different challenging scenarios presented below. In summary, in this thesis we address
several important points, enumerated (a)–(f), that currently represent a challenge in
Bayesian inference via Monte Carlo. A first challenge that we address is the problematic
exploration of the parameter space by off-the-shelf MCMC algorithms when there
is (a) multimodality, or with (b) highly concentrated posteriors. Another challenge that
we address is the (c) proposal construction in IS. Furtheremore, in recent applications we
need to deal with (d) expensive posteriors, and/or we need to handle (e) noisy posteriors.
Finally, the Bayesian framework also offers a way of comparing competing hypothesis
(models) in a principled way by means of marginal likelihoods. Hence, a task that arises
as of fundamental importance is (f) marginal likelihood computation.
Chapters 2 and 3 deal with (a), (b), and (c). In Chapter 2, we propose a novel population
MCMC algorithm called Parallel Metropolis-Hastings Coupler (PMHC). PMHC is
very suitable for multimodal scenarios since it works with a population of states, instead
of a single one, hence allowing for sharing information. PMHC combines independent
exploration by the use of parallel Metropolis-Hastings algorithms, with cooperative exploration
by the use of a population MCMC technique called Normal Kernel Coupler.
In Chapter 3, population MCMC are combined with IS within the layered adaptive IS
(LAIS) framework. The combination of MCMC and IS serves two purposes. First, an
automatic proposal construction. Second, it aims at increasing the robustness, since the
MCMC samples are not used directly to form the sample approximation of the posterior.
The use of minibatches of data is proposed to deal with highly concentrated posteriors.
Other extensions for reducing the costs with respect to the vanilla LAIS framework, based on recycling and clustering, are discussed and analyzed.
Chapters 4, 5 and 6 deal with (c), (d) and (e). The use of nonparametric approximations
of the posterior plays an important role in the design of efficient Monte Carlo algorithms.
Nonparametric approximations of the posterior can be obtained using machine learning
algorithms for nonparametric regression, such as Gaussian Processes and Nearest Neighbors.
Then, they can serve as cheap surrogate models, or for building efficient proposal
distributions. In Chapter 4, in the context of expensive posteriors, we propose adaptive
quadratures of posterior expectations and the marginal likelihood using a sequential algorithm
that builds and refines a nonparametric approximation of the posterior. In Chapter
5, we propose Regression-based Adaptive Deep Importance Sampling (RADIS), an adaptive
IS algorithm that uses a nonparametric approximation of the posterior as the proposal
distribution. We illustrate the proposed algorithms in applications of astronomy and remote
sensing. Chapter 4 and 5 consider noiseless posterior evaluations for building the
nonparametric approximations. More generally, in Chapter 6 we give an overview and
classification of MCMC and IS schemes using surrogates built with noisy evaluations.
The motivation here is the study of posteriors that are both costly and noisy. The classification
reveals a connection between algorithms that use the posterior approximation as a
cheap surrogate, and algorithms that use it for building an efficient proposal. We illustrate
specific instances of the classified schemes in an application of reinforcement learning.
Finally, in Chapter 7 we study noisy IS, namely, IS when the posterior evaluations are
noisy, and derive optimal proposal distributions for the different estimators in this setting.
Chapter 8 deals with (f). In Chapter 8, we provide with an exhaustive review of methods
for marginal likelihood computation, with special focus on the ones based on Monte
Carlo. We derive many connections among the methods and compare them in several
simulations setups. Finally, in Chapter 9 we summarize the contributions of this thesis
and discuss some potential avenues of future research.Programa de Doctorado en Ingeniería Matemática por la Universidad Carlos III de MadridPresidente: Valero Laparra Pérez-Muelas.- Secretario: Michael Peter Wiper.- Vocal: Omer Deniz Akyildi
The Iray Light Transport Simulation and Rendering System
While ray tracing has become increasingly common and path tracing is well
understood by now, a major challenge lies in crafting an easy-to-use and
efficient system implementing these technologies. Following a purely
physically-based paradigm while still allowing for artistic workflows, the Iray
light transport simulation and rendering system allows for rendering complex
scenes by the push of a button and thus makes accurate light transport
simulation widely available. In this document we discuss the challenges and
implementation choices that follow from our primary design decisions,
demonstrating that such a rendering system can be made a practical, scalable,
and efficient real-world application that has been adopted by various companies
across many fields and is in use by many industry professionals today
- …