Search results for "algorithm"
showing 10 items of 4887 documents
Using Chemical Structural Indicators for Periodic Classification of Local Anaesthetics
2011
Algorithms for classification and taxonomy based on criteria as information entropy and its production are proposed. Some local anaesthetics, currently in use, are classified using five characteristic chemical properties of different portions of their molecules. Many classification algorithms are based on information entropy. When applying the procedures to sets of moderate size, an excessive number of results appear compatible with data and the number suffers a combinatorial explosion. However, after the equipartition conjecture one has a selection criterion between different variants resulting from classification between hierarchical trees. Information entropy and principal component anal…
Conditional convex orders and measurable martingale couplings
2014
Strassen's classical martingale coupling theorem states that two real-valued random variables are ordered in the convex (resp.\ increasing convex) stochastic order if and only if they admit a martingale (resp.\ submartingale) coupling. By analyzing topological properties of spaces of probability measures equipped with a Wasserstein metric and applying a measurable selection theorem, we prove a conditional version of this result for real-valued random variables conditioned on a random element taking values in a general measurable space. We also provide an analogue of the conditional martingale coupling theorem in the language of probability kernels and illustrate how this result can be appli…
Markov Chain Monte Carlo Methods for High Dimensional Inversion in Remote Sensing
2004
SummaryWe discuss the inversion of the gas profiles (ozone, NO3, NO2, aerosols and neutral density) in the upper atmosphere from the spectral occultation measurements. The data are produced by the ‘Global ozone monitoring of occultation of stars’ instrument on board the Envisat satellite that was launched in March 2002. The instrument measures the attenuation of light spectra at various horizontal paths from about 100 km down to 10–20 km. The new feature is that these data allow the inversion of the gas concentration height profiles. A short introduction is given to the present operational data management procedure with examples of the first real data inversion. Several solution options for…
Spatio‐temporal classification in point patterns under the presence of clutter
2019
We consider the problem of detection of features in the presence of clutter for spatio-temporal point patterns. In previous studies, related to the spatial context, Kth nearest-neighbor distances to classify points between clutter and features. In particular, a mixture of distributions whose parameters were estimated using an expectation-maximization algorithm. This paper extends this methodology to the spatio-temporal context by considering the properties of the spatio-temporal Kth nearest-neighbor distances. For this purpose, we make use of a couple of spatio-temporal distances, which are based on the Euclidean and the maximum norms. We show close forms for the probability distributions o…
BGSA: a bit-parallel global sequence alignment toolkit for multi-core and many-core architectures
2018
Abstract Motivation Modern bioinformatics tools for analyzing large-scale NGS datasets often need to include fast implementations of core sequence alignment algorithms in order to achieve reasonable execution times. We address this need by presenting the BGSA toolkit for optimized implementations of popular bit-parallel global pairwise alignment algorithms on modern microprocessors. Results BGSA outperforms Edlib, SeqAn and BitPAl for pairwise edit distance computations and Parasail, SeqAn and BitPAl when using more general scoring schemes for pairwise alignments of a batch of sequence reads on both standard multi-core CPUs and Xeon Phi many-core CPUs. Furthermore, banded edit distance perf…
A penalized approach to covariate selection through quantile regression coefficient models
2019
The coefficients of a quantile regression model are one-to-one functions of the order of the quantile. In standard quantile regression (QR), different quantiles are estimated one at a time. Another possibility is to model the coefficient functions parametrically, an approach that is referred to as quantile regression coefficients modeling (QRCM). Compared with standard QR, the QRCM approach facilitates estimation, inference and interpretation of the results, and generates more efficient estimators. We designed a penalized method that can address the selection of covariates in this particular modelling framework. Unlike standard penalized quantile regression estimators, in which model selec…
Calibrating a microscopic traffic simulation model for roundabouts using genetic algorithms
2018
The paper introduces a methodological approach based on genetic algorithms to calibrate microscopic traffic simulation models. The specific objective is to test an automated procedure utilizing genetic algorithms for assigning the most appropriate values to driver and vehicle parameters in AIMSUN. The genetic algorithm tool in MATLAB® and AIMSUN micro-simulation software were used. A subroutine in Python implemented the automatic interaction of AIMSUN with MATLAB®. Focus was made on two roundabouts selected as case studies. Empirical capacity functions based on summary random-effects estimates of critical headway and follow up headway derived from meta-analysis were used as reference for ca…
Quantitative characterization of antigens using monoclonal antibody reactivities
1993
A multipurpose program that empirically relates antigenic reactivities with monoclonal antibodies (MAbs) to genetic distances is presented. The program uses a set of known genetic pairwise distances to weigh each MAb depending on its capacity to define groups of taxonomically related antigens. This allows highly accurate identification and classification of unknown antigens. Also, the weights obtained constitute a quantitative measure of epitope conservation and can be used for improved vaccine design. © 1993 Oxford University Press.
Forecasting time series with missing data using Holt's model
2009
This paper deals with the prediction of time series with missing data using an alternative formulation for Holt's model with additive errors. This formulation simplifies both the calculus of maximum likelihood estimators of all the unknowns in the model and the calculus of point forecasts. In the presence of missing data, the EM algorithm is used to obtain maximum likelihood estimates and point forecasts. Based on this application we propose a leave-one-out algorithm for the data transformation selection problem which allows us to analyse Holt's model with multiplicative errors. Some numerical results show the performance of these procedures for obtaining robust forecasts.
Bayesian analysis and design for comparison of effect-sizes
2002
Comparison of effect-sizes, or more generally, of non-centrality parameters of non-central t distributions, is a common problem, especially in meta-analysis. The usual simplifying assumptions of either identical or non-related effect-sizes are often too restrictive to be appropriate. In this paper, the effect-sizes are modeled as random effects with t distributions. Bayesian hierarchical models are used both to design and analyze experiments. The main goal is to compare effect-sizes. Sample sizes are chosen so as to make accurate inferences about the difference of effect-sizes and also to convincingly solve the testing of equality of effect-sizes if such is the goal.