434 research outputs found
Stochastic expansions using continuous dictionaries: L\'{e}vy adaptive regression kernels
This article describes a new class of prior distributions for nonparametric
function estimation. The unknown function is modeled as a limit of weighted
sums of kernels or generator functions indexed by continuous parameters that
control local and global features such as their translation, dilation,
modulation and shape. L\'{e}vy random fields and their stochastic integrals are
employed to induce prior distributions for the unknown functions or,
equivalently, for the number of kernels and for the parameters governing their
features. Scaling, shape, and other features of the generating functions are
location-specific to allow quite different function properties in different
parts of the space, as with wavelet bases and other methods employing
overcomplete dictionaries. We provide conditions under which the stochastic
expansions converge in specified Besov or Sobolev norms. Under a Gaussian error
model, this may be viewed as a sparse regression problem, with regularization
induced via the L\'{e}vy random field prior distribution. Posterior inference
for the unknown functions is based on a reversible jump Markov chain Monte
Carlo algorithm. We compare the L\'{e}vy Adaptive Regression Kernel (LARK)
method to wavelet-based methods using some of the standard test functions, and
illustrate its flexibility and adaptability in nonstationary applications.Comment: Published in at http://dx.doi.org/10.1214/11-AOS889 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Fast and scalable non-parametric Bayesian inference for Poisson point processes
We study the problem of non-parametric Bayesian estimation of the intensity
function of a Poisson point process. The observations are independent
realisations of a Poisson point process on the interval . We propose two
related approaches. In both approaches we model the intensity function as
piecewise constant on bins forming a partition of the interval . In
the first approach the coefficients of the intensity function are assigned
independent gamma priors, leading to a closed form posterior distribution. On
the theoretical side, we prove that as the posterior
asymptotically concentrates around the "true", data-generating intensity
function at an optimal rate for -H\"older regular intensity functions (). In the second approach we employ a gamma Markov chain prior on the
coefficients of the intensity function. The posterior distribution is no longer
available in closed form, but inference can be performed using a
straightforward version of the Gibbs sampler. Both approaches scale well with
sample size, but the second is much less sensitive to the choice of .
Practical performance of our methods is first demonstrated via synthetic data
examples. We compare our second method with other existing approaches on the UK
coal mining disasters data. Furthermore, we apply it to the US mass shootings
data and Donald Trump's Twitter data.Comment: 45 pages, 22 figure
Blind image deconvolution: nonstationary Bayesian approaches to restoring blurred photos
High quality digital images have become pervasive in modern scientific and everyday life —
in areas from photography to astronomy, CCTV, microscopy, and medical imaging. However
there are always limits to the quality of these images due to uncertainty and imprecision in the
measurement systems. Modern signal processing methods offer the promise of overcoming
some of these problems by postprocessing
these blurred and noisy images. In this thesis,
novel methods using nonstationary statistical models are developed for the removal of blurs
from out of focus and other types of degraded photographic images.
The work tackles the fundamental problem blind image deconvolution (BID); its goal is
to restore a sharp image from a blurred observation when the blur itself is completely unknown.
This is a “doubly illposed”
problem — extreme lack of information must be countered
by strong prior constraints about sensible types of solution. In this work, the hierarchical
Bayesian methodology is used as a robust and versatile framework to impart the required prior
knowledge.
The thesis is arranged in two parts. In the first part, the BID problem is reviewed, along
with techniques and models for its solution. Observation models are developed, with an
emphasis on photographic restoration, concluding with a discussion of how these are reduced
to the common linear spatially-invariant
(LSI) convolutional model. Classical methods for the
solution of illposed
problems are summarised to provide a foundation for the main theoretical
ideas that will be used under the Bayesian framework. This is followed by an indepth
review
and discussion of the various prior image and blur models appearing in the literature, and then
their applications to solving the problem with both Bayesian and nonBayesian
techniques.
The second part covers novel restoration methods, making use of the theory presented in Part I.
Firstly, two new nonstationary image models are presented. The first models local variance in
the image, and the second extends this with locally adaptive noncausal
autoregressive (AR)
texture estimation and local mean components. These models allow for recovery of image
details including edges and texture, whilst preserving smooth regions. Most existing methods
do not model the boundary conditions correctly for deblurring of natural photographs, and a
Chapter is devoted to exploring Bayesian solutions to this topic.
Due to the complexity of the models used and the problem itself, there are many challenges
which must be overcome for tractable inference. Using the new models, three different inference
strategies are investigated: firstly using the Bayesian maximum marginalised a posteriori
(MMAP) method with deterministic optimisation; proceeding with the stochastic methods
of variational Bayesian (VB) distribution approximation, and simulation of the posterior distribution
using the Gibbs sampler. Of these, we find the Gibbs sampler to be the most effective
way to deal with a variety of different types of unknown blurs. Along the way, details are given
of the numerical strategies developed to give accurate results and to accelerate performance.
Finally, the thesis demonstrates state of the art
results in blind restoration of synthetic and real
degraded images, such as recovering details in out of focus photographs
Bayesian wavelet de-noising with the caravan prior
According to both domain expert knowledge and empirical evidence, wavelet
coefficients of real signals tend to exhibit clustering patterns, in that they
contain connected regions of coefficients of similar magnitude (large or
small). A wavelet de-noising approach that takes into account such a feature of
the signal may in practice outperform other, more vanilla methods, both in
terms of the estimation error and visual appearance of the estimates. Motivated
by this observation, we present a Bayesian approach to wavelet de-noising,
where dependencies between neighbouring wavelet coefficients are a priori
modelled via a Markov chain-based prior, that we term the caravan prior.
Posterior computations in our method are performed via the Gibbs sampler. Using
representative synthetic and real data examples, we conduct a detailed
comparison of our approach with a benchmark empirical Bayes de-noising method
(due to Johnstone and Silverman). We show that the caravan prior fares well and
is therefore a useful addition to the wavelet de-noising toolbox.Comment: 32 pages, 15 figures, 4 table
Dynare: Reference Manual Version 4
Dynare is a software platform for handling a wide class of economic models, in particular dynamic stochastic general equilibrium (DSGE) and overlapping generations (OLG) models. The models solved by Dynare include those relying on the rational expectations hypothesis, wherein agents form their expectations about the future in a way consistent with the model. But Dynare is also able to handle models where expectations are formed differently: on one extreme, models where agents perfectly anticipate the future; on the other extreme, models where agents have limited rationality or imperfect knowledge of the state of the economy and, hence, form their expectations through a learning process. Dynare offers a user-friendly and intuitive way of describing these models. It is able to perform simulations of the model given a calibration of the model parameters and is also able to estimate these parameters given a dataset. Dynare is a free software, which means that it can be downloaded free of charge, that its source code is freely available, and that it can be used for both non-profit and for-profit purposes.Dynare; Numerical methods; Perturbation; Rational expectations
- …