39,299 research outputs found
An LP-Based Approach for Goal Recognition as Planning
Goal recognition aims to recognize the set of candidate goals that are
compatible with the observed behavior of an agent. In this paper, we develop a
method based on the operator-counting framework that efficiently computes
solutions that satisfy the observations and uses the information generated to
solve goal recognition tasks. Our method reasons explicitly about both partial
and noisy observations: estimating uncertainty for the former, and satisfying
observations given the unreliability of the sensor for the latter. We evaluate
our approach empirically over a large data set, analyzing its components on how
each can impact the quality of the solutions. In general, our approach is
superior to previous methods in terms of agreement ratio, accuracy, and spread.
Finally, our approach paves the way for new research on combinatorial
optimization to solve goal recognition tasks.Comment: 8 pages, 4 tables, 3 figures. Published in AAAI 2021. Updated final
authorship and tex
Confidence Statements for Ordering Quantiles
This work proposes Quor, a simple yet effective nonparametric method to
compare independent samples with respect to corresponding quantiles of their
populations. The method is solely based on the order statistics of the samples,
and independence is its only requirement. All computations are performed using
exact distributions with no need for any asymptotic considerations, and yet can
be run using a fast quadratic-time dynamic programming idea. Computational
performance is essential in high-dimensional domains, such as gene expression
data. We describe the approach and discuss on the most important assumptions,
building a parallel with assumptions and properties of widely used techniques
for the same problem. Experiments using real data from biomedical studies are
performed to empirically compare Quor and other methods in a classification
task over a selection of high-dimensional data sets
Bayesian Analysis of Simple Random Densities
A tractable nonparametric prior over densities is introduced which is closed
under sampling and exhibits proper posterior asymptotics.Comment: 19 pages; 6 figure
Predictive analysis of microarray data
Microarray gene expression data are analyzed by means of a Bayesian
nonparametric model, with emphasis on prediction of future observables,
yielding a method for selection of differentially expressed genes and a
classifier
The Likelihood Ratio Test and Full Bayesian Significance Test under small sample sizes for contingency tables
Hypothesis testing in contingency tables is usually based on asymptotic
results, thereby restricting its proper use to large samples. To study these
tests in small samples, we consider the likelihood ratio test and define an
accurate index, the P-value, for the celebrated hypotheses of homogeneity,
independence, and Hardy-Weinberg equilibrium. The aim is to understand the use
of the asymptotic results of the frequentist Likelihood Ratio Test and the
Bayesian FBST -- Full Bayesian Significance Test -- under small-sample
scenarios. The proposed exact P-value is used as a benchmark to understand the
other indices. We perform analysis in different scenarios, considering
different sample sizes and different table dimensions. The exact Fisher test
for tables that drastically reduces the sample space is also
discussed. The main message of this paper is that all indices have very similar
behavior, so the tests based on asymptotic results are very good to be used in
any circumstance, even with small sample sizes
- …