224 research outputs found
Likelihood informed dimension reduction for inverse problems in remote sensing of atmospheric constituent profiles
We use likelihood informed dimension reduction (LIS) (T. Cui et al. 2014) for
inverting vertical profile information of atmospheric methane from ground based
Fourier transform infrared (FTIR) measurements at Sodankyl\"a, Northern
Finland. The measurements belong to the word wide TCCON network for greenhouse
gas measurements and, in addition to providing accurate greenhouse gas
measurements, they are important for validating satellite observations. LIS
allows construction of an efficient Markov chain Monte Carlo sampling algorithm
that explores only a reduced dimensional space but still produces a good
approximation of the original full dimensional Bayesian posterior distribution.
This in effect makes the statistical estimation problem independent of the
discretization of the inverse problem. In addition, we compare LIS to a
dimension reduction method based on prior covariance matrix truncation used
earlier (S. Tukiainen et al. 2016)
Simulation and data processing of GOMOS measurements
In this paper the data simulation and data inversion studies for stellar occultation measurements are discussed. The specific application is the Global Ozone Monitoring by Occultation of Stars (GOMOS) instrument which has been proposed for the first European Platform, Polar Orbiting Earth Mission (POEM-1)
On the flexibility of the design of Multiple Try Metropolis schemes
The Multiple Try Metropolis (MTM) method is a generalization of the classical
Metropolis-Hastings algorithm in which the next state of the chain is chosen
among a set of samples, according to normalized weights. In the literature,
several extensions have been proposed. In this work, we show and remark upon
the flexibility of the design of MTM-type methods, fulfilling the detailed
balance condition. We discuss several possibilities and show different
numerical results
Kernel Sequential Monte Carlo
We propose kernel sequential Monte Carlo (KSMC), a framework for sampling from static target densities. KSMC is a family of
sequential Monte Carlo algorithms that are based on building emulator
models of the current particle system in a reproducing kernel Hilbert
space. We here focus on modelling nonlinear covariance structure and
gradients of the target. The emulator’s geometry is adaptively updated
and subsequently used to inform local proposals. Unlike in adaptive
Markov chain Monte Carlo, continuous adaptation does not compromise
convergence of the sampler. KSMC combines the strengths of sequental
Monte Carlo and kernel methods: superior performance for multimodal
targets and the ability to estimate model evidence as compared to Markov
chain Monte Carlo, and the emulator’s ability to represent targets that
exhibit high degrees of nonlinearity. As KSMC does not require access to
target gradients, it is particularly applicable on targets whose gradients
are unknown or prohibitively expensive. We describe necessary tuning
details and demonstrate the benefits of the the proposed methodology on
a series of challenging synthetic and real-world examples
Estimation of ECHAM5 climate model closure parameters with adaptive MCMC
Climate models contain closure parameters to which the model climate is sensitive. These parameters appear in physical parameterization schemes where some unresolved variables are expressed by predefined parameters rather than being explicitly modeled. Currently, best expert knowledge is used to define the optimal closure parameter values, based on observations, process studies, large eddy simulations, etc. Here, parameter estimation, based on the adaptive Markov chain Monte Carlo (MCMC) method, is applied for estimation of joint posterior probability density of a small number (<i>n</i>=4) of closure parameters appearing in the ECHAM5 climate model. The parameters considered are related to clouds and precipitation and they are sampled by an adaptive random walk process of the MCMC. The parameter probability densities are estimated simultaneously for all parameters, subject to an objective function. Five alternative formulations of the objective function are tested, all related to the net radiative flux at the top of the atmosphere. Conclusions of the closure parameter estimation tests with a low-resolution ECHAM5 climate model indicate that (i) adaptive MCMC is a viable option for parameter estimation in large-scale computational models, and (ii) choice of the objective function is crucial for the identifiability of the parameter distributions
An Adaptive Interacting Wang-Landau Algorithm for Automatic Density Exploration
While statisticians are well-accustomed to performing exploratory analysis in
the modeling stage of an analysis, the notion of conducting preliminary
general-purpose exploratory analysis in the Monte Carlo stage (or more
generally, the model-fitting stage) of an analysis is an area which we feel
deserves much further attention. Towards this aim, this paper proposes a
general-purpose algorithm for automatic density exploration. The proposed
exploration algorithm combines and expands upon components from various
adaptive Markov chain Monte Carlo methods, with the Wang-Landau algorithm at
its heart. Additionally, the algorithm is run on interacting parallel chains --
a feature which both decreases computational cost as well as stabilizes the
algorithm, improving its ability to explore the density. Performance is studied
in several applications. Through a Bayesian variable selection example, the
authors demonstrate the convergence gains obtained with interacting chains. The
ability of the algorithm's adaptive proposal to induce mode-jumping is
illustrated through a trimodal density and a Bayesian mixture modeling
application. Lastly, through a 2D Ising model, the authors demonstrate the
ability of the algorithm to overcome the high correlations encountered in
spatial models.Comment: 33 pages, 20 figures (the supplementary materials are included as
appendices
Immune-mediated competition in rodent malaria is most likely caused by induced changes in innate immune clearance of merozoites
Malarial infections are often genetically diverse, leading to competitive interactions between parasites. A quantitative understanding of the competition between strains is essential to understand a wide range of issues, including the evolution of virulence and drug resistance. In this study, we use dynamical-model based Bayesian inference to investigate the cause of competitive suppression of an avirulent clone of Plasmodium chabaudi (AS) by a virulent clone (AJ) in immuno-deficient and competent mice. We test whether competitive suppression is caused by clone-specific differences in one or more of the following processes: adaptive immune clearance of merozoites and parasitised red blood cells (RBCs), background loss of merozoites and parasitised RBCs, RBC age preference, RBC infection rate, burst size, and within-RBC interference. These processes were parameterised in dynamical mathematical models and fitted to experimental data. We found that just one parameter μ, the ratio of background loss rate of merozoites to invasion rate of mature RBCs, needed to be clone-specific to predict the data. Interestingly, μ was found to be the same for both clones in single-clone infections, but different between the clones in mixed infections. The size of this difference was largest in immuno-competent mice and smallest in immuno-deficient mice. This explains why competitive suppression was alleviated in immuno-deficient mice. We found that competitive suppression acts early in infection, even before the day of peak parasitaemia. These results lead us to argue that the innate immune response clearing merozoites is the most likely, but not necessarily the only, mediator of competitive interactions between virulent and avirulent clones. Moreover, in mixed infections we predict there to be an interaction between the clones and the innate immune response which induces changes in the strength of its clearance of merozoites. What this interaction is unknown, but future refinement of the model, challenged with other datasets, may lead to its discovery
A consistent analysis of three years of ground- and space-based photometry of TrES-2
The G0V dwarf TrES-2A, which is transited by a hot Jupiter, is one of the
main short-cadence targets of the Kepler telescope and, therefore, among the
photometrically best-studied planetary systems known today. Given the
near-grazing geometry of the planetary orbit, TrES-2 offers an outstanding
opportunity to search for changes in its orbital geometry. Our study focuses on
the secular change in orbital inclination reported in previous studies. We
present a joint analysis of the first four quarters of Kepler photometry
together with the publicly available ground-based data obtained since the
discovery of TrES-2b in 2006. We use a common approach based on the latest
information regarding the visual companion of TrES-2A and stellar limb
darkening to further refine the orbital parameters. We find that the Kepler
observations rule out a secular inclination change of previously claimed order
as well as variations of the transit timing, however, they also show slight
indication for further variability in the inclination which remains marginally
significant
A generative approach for image-based modeling of tumor growth
22nd International Conference, IPMI 2011, Kloster Irsee, Germany, July 3-8, 2011. ProceedingsExtensive imaging is routinely used in brain tumor patients to monitor the state of the disease and to evaluate therapeutic options. A large number of multi-modal and multi-temporal image volumes is acquired in standard clinical cases, requiring new approaches for comprehensive integration of information from different image sources and different time points. In this work we propose a joint generative model of tumor growth and of image observation that naturally handles multi-modal and longitudinal data. We use the model for analyzing imaging data in patients with glioma. The tumor growth model is based on a reaction-diffusion framework. Model personalization relies only on a forward model for the growth process and on image likelihood. We take advantage of an adaptive sparse grid approximation for efficient inference via Markov Chain Monte Carlo sampling. The approach can be used for integrating information from different multi-modal imaging protocols and can easily be adapted to other tumor growth models.German Academy of Sciences Leopoldina (Fellowship Programme LPDS 2009-10)Academy of Finland (133611)National Institutes of Health (U.S.) (NIBIB NAMIC U54-EB005149)National Institutes of Health (U.S.) (NCRR NAC P41- RR13218)National Institutes of Health (U.S.) (NINDS R01-NS051826)National Institutes of Health (U.S.) (NIH R01-NS052585)National Institutes of Health (U.S.) (NIH R01-EB006758)National Institutes of Health (U.S.) (NIH R01-EB009051)National Institutes of Health (U.S.) (NIH P41-RR014075)National Science Foundation (U.S.) (CAREER Award 0642971
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