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Probabilistic method - Wikipedia, the free encyclopedia This article is not about probabilistic algorithms, which give the right answer with high probability but not with certainty, nor about Monte Carlo methods, which are simulations relying on pseudo-randomness. The probabilistic method is a non-constructive method primarily used in combinatorics and pioneered by Paul Erdős, for proving the existence of a prescribed kind of mathematical object. Although others before him proved theorems via the probabilistic method (for example, Szele's 1943 result that there exist tournaments containing a large number of Hamiltonian cycles), many of the most well known proofs using this method are due to Erdős. www.wikipedia.org /wiki/Probabilistic_method   (953 words)

Probabilistic Causation The history of probabilistic causation is to a large extent a history of attempts to resolve these two central problems. The probabilistic theories of causation described in Section 3 above are suited to analyze the total or net effect of one factor or variable on other, whereas the causal modeling techniques discussed in this section are primarily geared toward decomposing a causal system into individual routes of causal influence. Given the basic probability-raising idea, one would expect putative counterexamples to probabilistic theories of causation to be of two basic types: cases where causes fail to raise the probabilities of their effects, and cases where non-causes raise the probabilities of non-effects. plato.stanford.edu /entries/causation-probabilistic   (10508 words)

AI Magazine: Probabilistic Algorithms in Robotics. @ HighBeam Research   (Site not responding. Last check: 2007-09-06) Probabilistic control: Autonomous robots must act in the face of uncertainty, a direct consequence of their inability to know what is the case. Because probabilistic models are insufficient to predict the actual state, sensor measurements play a vital role in state estimation and, thus, in the determination of a robot's actual behavior. Probabilistic algorithms are still far from mainstream in robotics, and a range of problems appear to be highly amenable to probabilistic solutions. www.highbeam.com /library/doc0.asp?DOCID=1G1:68999259&refid=holomed_1   (8930 words)

SPC Probabilistic Outlook Information The SPC produces probabilistic Convective Outlooks in conjunction with the traditional categorical Convective Outlooks. As a part of the probabilistic forecasting program at the SPC, a representative severe weather climatology has been developed by members of the National Severe Storms Laboratory (NSSL) and the SPC for use by the SPC, the emergency management community, and the general public. During this period, the SPC produces probabilistic outlooks for each primary severe weather hazard (tornadoes, damaging wind, and large hail) separately. www.spc.noaa.gov /products/outlook/probinfo.html   (2312 words)

Probabilistic Population Estimation Probabilistic Population Estimation is a statistical procedure that provides unduplicated counts of the number of children and adolescents who are represented in more than one data set without reference to personally identifying information (Banks and Pandiani, in press, a). When probabilistic estimates of incarceration subsequent to treatment (outcomes) are combined with probabilistic estimates of incarceration before treatment (access), a very powerful risk adjusted measure of program performance is the result. The ability of this statistic to provide probabilistic estimates (with known confidence intervals) of these basic parameters of service systems is particularly valuable where issues of confidentiality or organizational complexity limit the availability of unique identifiers, or the lack of adequate financial resources inhibits the development of comprehensive integrated data warehouses. www.thebristolobservatory.com /PPE.htm   (2003 words)

PROBABILISTIC SAFETY ASSESSMENT   (Site not responding. Last check: 2007-09-06) As the use of probabilistic risk analysis became more widespread, the safety authorities asked design engineers to introduce appropriate measures whenever such analyses indicated that the probability of an event occurring that might potentially have unacceptable consequences for the public and the environment was sufficiently high. One of the recommendations made after the accident was that probabilistic analysis techniques should be used to supplement conventional safety assessment procedures for nuclear power plants, and that probabilistic objectives should be developed in order to facilitate the determination of acceptable safety levels for nuclear facilities. An initial section known as a probabilistic assessment of initiating events, which is aimed at identifying and estimating the frequencies of initiating events that might lead to severe core damage, or even meltdown, as a result of either a safety system failure or human error. www.nuce.boun.edu.tr /psaover.html   (2787 words)

Probabilistic IR   (Site not responding. Last check: 2007-09-06) For instance, [van Rijsbergen, 1979] and [Salton, 1989] both mention probabilistic indexing and describe it in some detail, but their models of probabilistic term weighting are quite different. The probabilistic models of IR differ in one important feature: Some of them use relevance information for computing the weights for a given document whereas other approaches, like [Hiemstra and Kraaij], don't need any relevance information. There are three areas in which probabilistic theory can be relevant for IR: in document ranking, in term weighting and in the similarity function itself. pi0959.kub.nl /Paai/Onderw/V-I/Content/probabilistic.html   (2084 words)

Machine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms In probabilistic information retrieval, the goal is to estimate the probability of relevance of a given document to a user with respect to a given query. Probabilistic assumptions about the distribution of elements in the representations within relevant and irrelevant documents are required. Although relevance feedback and probabilistic models exhibit interesting query or document refinement capabilities, their abstraction processes are based on either simple addition/removal of terms or probabilistic assumptions and principles. ai.bpa.arizona.edu /papers/mlir93/mlir93.html   (12851 words)

Link Prediction and Path Analysis Using Markov Chains The client sends a request to the web server (or proxy) which uses the HTTP probabilistic link prediction module in order to predict the probabilities of the next requests from the same user based on the history of requests from that client. Link prediction is used to build a navigation agent which suggests (to the user) which other sites/links would be of interest to the user based on the statistics of previous visits (either by this particular user or a collection of users'). However, there is no probabilistic weighting of the paths: the maximal matching most-frequent prefix is used to predict the next request.[Kraiss & Weikum 98] apply continuous Markov chains to influence caching priorities between primary, secondary and tertiary storages, and report experimental results on synthetic workloads. www9.org /w9cdrom/68/68.html   (5028 words)

DAGS - Daphne Koller's Research Group. Probabilistic Relational Models Page. Probabilistic Relational Models (PRMs) are a language based on relational logic for describing statistical models of structured data. Selectivity Estimation using Probabilistic Models L. Getoor, B. Taskar, and D. Koller, To appear in Proceedings of the ACM SIGMOD International Conference on Management of Data, Santa Barbara, California, May 2001. SPOOK: A system for probabilistic object-oriented knowledge representation A. Pfeffer, D. Koller, B. Milch, and K. Takusagawa, In Proceedings of the 15th Annual Conference on Uncertainty in AI (UAI), Stockholm, Sweden, August 1999, pages 541--550. dags.stanford.edu /PRMs   (396 words)

CFP: Workshop on Probabilistic Graphical Models for Classification (within ECML/PKDD'03) Probabilistic graphical model paradigm has become a popular tool for encoding, representing and handling uncertain knowledge in expert systems over the last decade. Currently, interest is emerging within probabilistic graphical models to use them as a tool to induce supervised-unsupervised classification models. The workshop audience is intended for researchers in the area of machine learning and probabilistic graphical models, including practitioners of knowledge discovery in databases and statistical and computational learning theorists. www.sc.ehu.es /ccwbayes/ecml-pkdd-03-workshop/call.htm   (1102 words)

Citations: Skou: Compositional Verification of Probabilistic Processes - Larsen (ResearchIndex)   (Site not responding. Last check: 2007-09-06) Several probabilistic logics have been proposed which allow to specify properties of the form the system satisfies property # with probability at least p where p is a real number in the interval [0,1] see e.g. or processes of a calculus with a probabilistic shuffle operator [2, 24] For instance, the transition probabilities for the synchronous parallel composition P 1 Theta P 2 of two sequential randomized processes P 1 and P 2 (each of them modelled by a fully probabilistic LTS) are obtained by. Several probabilistic logics have been proposed which allow to specify properties of the form the system satisfies property with probability at least p where p is a real number in the interval [0,1] see e.g. citeseer.ist.psu.edu /context/532858/0   (1429 words)

John Franco Probabilistic analysis of a generalization of the unit clause selection heuristic for the k-satisfiability problem. Probabilistic analysis of the unit clause and maximum occurring literal selection heuristics for the 3-Satisfiability problem, Conference of Approximately Solved Problems, Columbia University, New York, (1985). Probabilistic Analysis of Algorithms for the Satisfiability Problem, at the Workshop on Mathematical Methods in Artificial Intelligence, Ulm, West Germany (December, 1988). www.ececs.uc.edu /~franco   (1940 words)

Probability Forecasting We are going to consider the verification of probabilistic forecasts and not consider verification of dichotomous forecasts (the latter of which we believe to be a less than satisfactory approach for meteorologists to take). Naturally, this brings up the subject of "hedging." Some might interpret a probabilistic forecast as a hedge, and that is not an unreasonable position, from at least some viewpoints. It is imperative that this feedback be as rapid as possible, given the constraint that a useful evaluation of probabilistic forecasts requires a reasonably large ensemble of forecasts. www.nssl.noaa.gov /~brooks/prob/Probability.html   (6458 words)

Probabilistic Design Probabilistic engineering design makes calculations with the probability distributions of the design parameters, instead of the mean or nominal values only. If you are interested in probabilistic and robust design you might be interested in looking at my course notes. Stage 5, the determination of the probability distributions of the design parameters, is not a trivial exercise to do well, since there is often very little information to be had about the probability distributions of the parameters of a design (which most likely has not been built yet). www.ses.swin.edu.au /homes/browne/probabilisticdesign   (867 words)

Amazon.com: Books: Biological Sequence Analysis : Probabilistic Models of Proteins and Nucleic Acids   (Site not responding. Last check: 2007-09-06) Probabilistic modeling has been applied to many different areas, including speech recognition, network performance analysis, and computational radiology. An overview of probabilistic modeling is given in the first chapter, and the authors effectively introduce the concepts without heavy abstract formalism, which for completeness they delegate to the last chapter of the book. On the other hand, the probabilistic foundations of the different techniques is well written. www.amazon.com /exec/obidos/tg/detail/-/0521629713?v=glance   (2602 words)

Amos Storkey - Research - Belief Networks A given belief network is very easily related to a particular probabilistic model, and is easily understood in turns of direct dependence. Probabilistic relationships do not imply causality, even if the belief network was constructed using prior causal information. Because the Bayesian method is probabilistic, this amounts to finding the probability P(QD) that the questions Q have certain answers given the data D, which defines the knowledge about the specific circumstances. www.anc.ed.ac.uk /~amos/belief.html   (3548 words)

ICS 274: Probabilistic Learning: Theory and Algorithms Probabilistic learning is a key component in many areas within modern computer science, including artificial intelligence, data mining, speech recognition, computer vision, bioinformatics, and so forth. Topics covered will include probabilistic modeling, defining likelihoods, parameter estimation using likelihood and Bayesian techniques, probabilistic approaches to classification, clustering, and regression, and related topics such as model selection, bias/variance, and density estimation. Although it is ostensibly directed at neural network modeling, the text covers quite a large part of standard probabilistic learning methods such as density estimation, mixture models, estimation techniques (maximum likelihood and Bayesian methods), and bias-variance tradeoffs. www.ics.uci.edu /~smyth/courses/ics274   (1099 words)

[No title] The reason for this is simply that the bulk of the work in IR is non-probabilistic, and it is only recently that some significant headway has been made with probabilistic methods[1,2,3]. In Chapter 2 I dealt with automatic indexing based on a probabilistic model of the distribution of word tokens within a document (text); here I will be concerned with the distribution of index terms over the set of documents making up a collection or file. Hughes[18] shows that for a very general probabilistic structure the number of measurements is surprisingly small even though reasonably sized samples are used to 'train' the decision function. www.dcs.gla.ac.uk /Keith/Chapter.6/Ch.6.html   (11062 words)

Padhraic Smyth Probabilistic and statistical methods, in particular, are central to our research, providing both a sound theoretical basis and a practical framework for developing useful data analysis algorithms. Probabilistic modeling of large transaction data sets An investigation of maximum entropy and other probabilistic modeling techniques to the problem of generating approximate answers to interactive queries in a model-based manner for large transaction data sets (joint work with Nokia Research). Segmental semi-Markov models for waveform and signal modeling Both change-point detection and probabilistic pattern-finding are investigated and applied to a real-world problem in semiconductor manufacturing (joint work with KLA-Tencor and LAM Research). www.ics.uci.edu /%7Esmyth   (895 words)

probabilistic Turing machine   (Site not responding. Last check: 2007-09-06) Note: The typical, deterministic Turing machine (TM) can be seen as a probabilistic TM with no more than one alternative for each transition. A nondeterministic TM is a probabilistic TM ignoring the probabilities. Algorithms and Theory of Computation Handbook, CRC Press LLC, 1999, "probabilistic Turing machine", from Dictionary of Algorithms and Data Structures, Paul E. Black, ed., NIST. www.nist.gov /dads/HTML/probablturng.html   (158 words)

Probabilistic inference   (Site not responding. Last check: 2007-09-06) In recent years, computational methods for probabilistic inference has received a lot of attention, especially in the field of probabilistic expert systems based on the notion of causal structure. We have developed a propagation method for probabilistic networks, that through local operations in the network provides exact probabilistic inference if the probabilistic network is acyclic. The algorithm operates by local operations on the probabilistic network, and is therefore possible to run also for networks with cycles, and we have investigated its performance for approximate probabilistic inference on unrestricted networks. www.cs.chalmers.se /Cs/Research/Algorithms/AI/inference.html   (320 words)

IBFI Schloss Dagstuhl - Dagstuhl Seminar 05051 In this context, the terms probabilistic and statistical refer to the use of probabilistic representations and reasoning mechanisms grounded in probability theory, such as Bayesian networks, hidden Markov models and probabilistic grammars and the use of statistical learning and inference techniques. Probabilistic, logical and relational learning arises in many different subfields and applications of artificial intelligence and computer science, including: planning, reinforcement learning, probabilistic modeling, bio-informatics, natural language processing, etc. The interest in this problem can also be explained by the growing body of work that has addressed pair-wise intersections of these domains: The result should be a state-of-the-art overview of probabilistic, logical and relational learning as well as a research plan that identifies the key research themes and opportunities for the next few years. www.dagstuhl.de /05051   (864 words)

Probability Forecasting - 2 These have a probabilistic flavor, but their definitions are pretty vague and do not even have a clear documentation among SPC forecasters, much less their users. With respect to PoPs, people are confused about whether or not it's a probability of a rain event within their backyard, or a rain event within the 8 inch rain gage at the airport, or if it refers to the percent of the area that's going to experience rainfall, or whatever. Public education and awareness campaigns would be needed prior to introducing probabilistic severe convective weather forecasts, so that everyone would be as aware as possible of what "an event" means when we talk of the probability of an event. www.cimms.ou.edu /~doswell/probability/Probability_2.html   (6501 words)

Probabilistic Equivalence   (Site not responding. Last check: 2007-09-06) Clearly the important term in the phrase is "probabilistic". In more concrete terms, probabilistic equivalence means that we know perfectly the odds that we will find a difference between two groups. We achieve probabilistic equivalence through the mechanism of random assignment to groups. www.socialresearchmethods.net /kb/expequi.htm   (433 words)

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