4,783 research outputs found
Considerate Approaches to Achieving Sufficiency for ABC model selection
For nearly any challenging scientific problem evaluation of the likelihood is
problematic if not impossible. Approximate Bayesian computation (ABC) allows us
to employ the whole Bayesian formalism to problems where we can use simulations
from a model, but cannot evaluate the likelihood directly. When summary
statistics of real and simulated data are compared --- rather than the data
directly --- information is lost, unless the summary statistics are sufficient.
Here we employ an information-theoretical framework that can be used to
construct (approximately) sufficient statistics by combining different
statistics until the loss of information is minimized. Such sufficient sets of
statistics are constructed for both parameter estimation and model selection
problems. We apply our approach to a range of illustrative and real-world model
selection problems
On the accuracy of phase-type approximations of heavy-tailed risk models
Numerical evaluation of ruin probabilities in the classical risk model is an
important problem. If claim sizes are heavy-tailed, then such evaluations are
challenging. To overcome this, an attractive way is to approximate the claim
sizes with a phase-type distribution. What is not clear though is how many
phases are enough in order to achieve a specific accuracy in the approximation
of the ruin probability. The goals of this paper are to investigate the number
of phases required so that we can achieve a pre-specified accuracy for the ruin
probability and to provide error bounds. Also, in the special case of a
completely monotone claim size distribution we develop an algorithm to estimate
the ruin probability by approximating the excess claim size distribution with a
hyperexponential one. Finally, we compare our approximation with the heavy
traffic and heavy tail approximations.Comment: 24 pages, 13 figures, 8 tables, 38 reference
Information retrieval in multimedia databases using relevance feedback algorithms. Applying logistic regression to relevance feedback in image retrieval systems
This master tesis deals with the problem of image retrieval from large image databases. A
particularly interesting problem is the retrieval of all images which are similar to one in the user's mind,
taking into account his/her feedback which is expressed as positive or negative preferences for the images that
the system progressively shows during the search. Here, a novel algorithm is presented for the incorporation
of user preferences in an image retrieval system based exclusively on the visual content of the image, which is
stored as a vector of low-level features. The algorithm considers the probability of an image belonging to the
set of those sought by the user, and models the logit of this probability as the output of a linear model whose
inputs are the low level image features. The image database is ranked by the output of the model and shown
to the user, who selects a few positive and negative samples, repeating the process in an iterative way until
he/she is satisfied. The problem of the small sample size with respect to the number of features is solved by
adjusting several partial linear models and combining their relevance probabilities by means of an ordered
weighted averaged (OWA) operator. Experiments were made with 40 users and they exhibited good
performance in finding a target image (4 iterations on average) in a database of about 4700 imagesZuccarello, PD. (2007). Information retrieval in multimedia databases using relevance feedback algorithms. Applying logistic regression to relevance feedback in image retrieval systems. http://hdl.handle.net/10251/12196Archivo delegad
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