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Nonsystematic Backtracking Search
, 1995
"... Many practical problems in Artificial Intelligence have search trees that are too large to search exhaustively in the amount of time allowed. Systematic techniques such as chronological backtracking can be applied to these problems, but the order in which they examine nodes makes them unlikely to fi ..."
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Cited by 57 (1 self)
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Many practical problems in Artificial Intelligence have search trees that are too large to search exhaustively in the amount of time allowed. Systematic techniques such as chronological backtracking can be applied to these problems, but the order in which they examine nodes makes them unlikely to find a solution in the explored fraction of the space. Nonsystematic techniques have been proposed to alleviate the problem by searching nodes in a random order. A technique known as iterative sampling follows random paths from the root of the tree to the fringe, stopping if a path ends at a goal node. Although the nonsystematic techniques do not suffer from the problem of exploring nodes in a bad order, they do reconsider nodes they have already ruled out, a problem that is serious when the density of solutions in the tree is low. Unfortunately, for many practical problems the order of examing nodes matters and the density of solutions is low. Consequently, neither chronological backtracking...
Stochastic Modeling of Early Hematopoiesis
"... Hematopoiesis is the body's way of making the cellular constituents of blood. Oxygen transport, response to infections, and control of bleeding are among the functions of different mature blood cells. These specific functions are acquired as cells mature in the bone marrow. Stem cells are the & ..."
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Cited by 2 (0 self)
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Hematopoiesis is the body's way of making the cellular constituents of blood. Oxygen transport, response to infections, and control of bleeding are among the functions of different mature blood cells. These specific functions are acquired as cells mature in the bone marrow. Stem cells are the "master cells" at the top of this pedigree, having within them the capacity to reconstitute the entire system. While the latter stages of hematopoiesis are fairly well understood, the functioning of stem cells and other multipotential cells is currently a matter of intense research. This paper presents a statistical analysis providing support for the clonal succession model of early hematopoiesis. J. L. Abkowitz and colleagues at the University of Washington have developed an experimental method for studying the kinetics of early hematopoiesis in a hybrid cat. The essence of the method is to analyze G6PD, an enzyme linked to the Xchromosome. The G6PD type of a cell forms a binary marker that is ...
A Virulence and Antimicrobial Resistance DNA Microarray Detects a High Frequency of Virulence Genes in Escherichia coli Isolates from Great Lakes Recreational Waters
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Extremes Directional models Design criteria
, 2008
"... Statistical estimation of extreme ocean environments: The requirement for ..."
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Statistical estimation of extreme ocean environments: The requirement for
Statistical methods for high throughput . . .
, 2005
"... High Throughput Screening (HTS) is used in drug discovery to screen large numbers of compounds against a biological target. Data on activity against the target are collected for a representative sample of compounds selected from a large library. The goal of drug discovery is to relate the activity ..."
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High Throughput Screening (HTS) is used in drug discovery to screen large numbers of compounds against a biological target. Data on activity against the target are collected for a representative sample of compounds selected from a large library. The goal of drug discovery is to relate the activity of a compound to its chemical structure, which is quantified by various explanatory variables, and hence to identify further active compounds. Often, this application has a very unbalanced class distribution, with a rare active class. Classification methods are commonly proposed as solutions to this problem. However, regarding drug discovery, researchers are more interested in ranking compounds by predicted activity than in the classification itself. This feature makes my approach distinct from common classification techniques. In this
Frequency Estimation of Internet Packet Streams with Limited Space ∗
"... We consider a router on the Internet analyzing the statistical properties of a TCP/IP packet stream. A fundamental difficulty with measuring traffic behavior on the Internet is that there is simply too much data to be recorded for later analysis, on the order of gigabytes a second. As a result, netw ..."
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We consider a router on the Internet analyzing the statistical properties of a TCP/IP packet stream. A fundamental difficulty with measuring traffic behavior on the Internet is that there is simply too much data to be recorded for later analysis, on the order of gigabytes a second. As a result, network routers can collect only relatively few statistics about the data. The central problem addressed here is to use the limited memory of routers to determine essential features of the network traffic stream. A particularly difficult and representative subproblem is to determine the top k categories to which the most packets belong, for a desired value of k and for a given notion of categorization such as the destination IP address. We present an algorithm that deterministically finds (in particular) all categories having a frequency above 1/(m + 1) using m counters, which we prove is best possible in the worst case. We also present a samplingbased algorithm for the case that packet categories follow an arbitrary distribution, but their order over time is permuted uniformly at random. Under this model, our algorithm identifies flows above a frequency threshold of roughly 1 / √ nm with high probability, where m is the number of counters and n is the number of packets observed. This guarantee is not far off from the ideal of identifying all flows (probability 1/n), and we prove that it is best possible up to a logarithmic factor. We show that the algorithm ranks the identified flows according to frequency within any desired constant factor of accuracy. 1
Bayesian Evaluation of NonAdmissible Conditioning: The Case of Fisher Test
, 1998
"... We first analyse the general problem of admissible conditioning and next consider the evaluation of the loss of information when a nonadmissible conditioning is used as an approximation of the exact posterior distribution. Considering the case of Fisher test, we evaluate from a Bayesian point o ..."
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We first analyse the general problem of admissible conditioning and next consider the evaluation of the loss of information when a nonadmissible conditioning is used as an approximation of the exact posterior distribution. Considering the case of Fisher test, we evaluate from a Bayesian point of view how much information is lost when the sampling process for a 2x2 contingency table is analysed conditionally on the two margins. This loss of information due to nonadmissible conditioning is evaluated for different sampling models and with respect to the entropy divergence and to the Hellinger distance between the exact and the approximate posterior distributions and with respect to relative risks based on a quadratic loss function. The numerical results obtained through simulation indicate that for a specific range of parameters the loss of information increases with the sample size and decreases with the precision of the a priori distribution. Hence such an approximation is shown to be a nonasymptotic one. Keywords: Approximate Bayesian Solutions, Admissible conditioning, Contingency tables, Fisher Test. Acknowledgements : Research supports from the contract "Projet d'Actions de Recherche Concert'ees" No. 93/98164, from the Belgian Program on Interuniversity Poles of Attraction (No. 26), from the Belgian OSTC Transport and Mobility (contract TRB3/007), from the Departmental fellowship of UCL and from DIDUACh are gratefully acknowledged. This version has benefited from many useful discussions with J.M. Rolin and E. San Mart'in. 1 CORE and Institut de Statistique, Universit'e Catholique de Louvain, LouvainlaNeuve, Belgium. 2 Instituto de Inform'atica, Universidad Austral de Chile, Valdivia, Chile 1 1
Jacobus van Zyl, B.Sc. B.Eng. M.Sc.
"... In 1965 Lofti A. Zadeh proposed fuzzy sets as a generalization of crisp (or classic) sets to address the incapability of crisp sets to model uncertainty and vagueness inherent in the real world. Initially, fuzzy sets did not receive a very warm welcome as many academics stood skeptical towards a the ..."
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In 1965 Lofti A. Zadeh proposed fuzzy sets as a generalization of crisp (or classic) sets to address the incapability of crisp sets to model uncertainty and vagueness inherent in the real world. Initially, fuzzy sets did not receive a very warm welcome as many academics stood skeptical towards a theory of “imprecise ” mathematics. In the middle to late 1980’s the success of fuzzy controllers brought fuzzy sets into the limelight, and many applications using fuzzy sets started appearing. In the early 1970’s the first machine learning algorithms started appearing. The AQ (for Aq) family of algorithms pioneered by Ryszard S. Michalski is a good example of the family of set covering algorithms. This class of learning algorithm induces concept descriptions by a greedy construction of rules that describe (or cover) positive training examples but not negative training examples. The learning process is iterative, and in each iteration one rule is induced and the positive examples covered by the rule removed from the set of positive training examples. Because positive instances are separated from negative instances, the term separateandconquer has been used to contrast the learning strategy against decision tree induction that use a divideandconquer learning strategy. This dissertation proposes fuzzy set covering as a powerful rule induction strategy. We survey existing