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Topic: Probabilistic methods

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	EOM Current Issues
		Although this traditional method has the advantage of being based upon field observations and, in the best cases, years of professional experience, it is also a highly subjective method with a limited capacity to deal with unprecedented conditions such as major earthquakes, major rainstorms, or profound land-use changes.
		Rational probabilistic methods provide an alternative to scenario modeling because they are designed to incorporate parameter uncertainty and variability into their input and output.
		The use of a mechanically based model of slope stability is an important difference between rational and empirical probabilistic methods, and allows the rational models to be used to assess the impact of rare or unprecedented conditions that fall outside the bounds of empirical models.
	www.eomonline.com /Common/currentissues/Dec01/haneberg.htm (1887 words)

	Monte Carlo method - Wikipedia, the free encyclopedia
		Monte Carlo simulation methods are especially useful in studying systems with a large number of coupled degrees of freedom, such as liquids, disordered materials, strongly coupled solids, and cellular structures (see cellular Potts model).
		Probabilistic formulation of inverse problems leads to the definition of a probability distribution in the model space.
		Uses of Monte Carlo methods require large amounts of random numbers, and it was their use that spurred the development of pseudorandom number generators, which were far quicker to use than the tables of random numbers which had been previously used for statistical sampling.
	en.wikipedia.org /wiki/Monte_Carlo_method (2216 words)

	LONI \| Atlases \| Atlasing Methods \| Probabilistic Atlases
		Probabilistic atlasing is a research strategy whose goal is to generate anatomical templates that retain quantitative information on inter-subject variations in brain architecture.
		A digital probabilistic atlas of the human brain, incorporating precise statistical information on positional variability of important functional and anatomic interfaces, may rectify many current atlasing problems, since it specifically stores information on the population variability.
		Methods to create probabilistic brain representations currently fall into three major categories, each differing slightly in its conceptual foundations.
	www.loni.ucla.edu /Atlases/Atlasing_Probabilistic.jsp (102 words)

	A review of probabilistic risk assessment of contaminated land
		This can be achieved by applying probabilistic methods which, although they have been available for many years, are still not generally used.
		Probabilistic risk assessments have been used in widely different settings, such as the metallurgical industry (mining and smelting operations), manufacturing, gas plants, wood impregnation, infrastructure, and waste landfills.
		Probabilistic risk assessment is used to derive soil guideline values in the United Kingdom, and other countries may be anticipated to follow.
	www.tomasoberg.com /english/abstract82_en.htm (712 words)

	Antimeta: Probabilistic Methods in Mathematics
		This method is not related to what I was talking about before, because it gives a deductively valid proof of the result, though it uses facts about probabilities in intermediate stages.
		Both of these last two methods may be useful for raising one's subjective credence in some result on a Bayesian account of mathematics (suitably modified to avoid the fact that standard Bayesian epistemology implies logical omniscience).
		Your example of why probabilistic proofs of primality is not well-accepted in mathematics reminds me of the reason why computer scientists try to "derandomize" randomized algorithms, the best such example being the recently discovered algorithm that witnesses that primality is polynomial-time computable.
	www.ocf.berkeley.edu /~easwaran/blog/2005/12/probabilistic_methods_in_mathe.html (1568 words)

	Computational Methods for Probabilistic Decision Trees -- from Mathematica Information Center
		Both algebraic approximations and Monte Carlo simulation methods were used; in particular, simulations with beta, logistic-normal, and triangular distributions for branching probabilities were compared.
		Algebraic and simulation methods of sensitivity analysis were also implemented and compared.
		Computation is no longer a significant barrier to the use of probabilistic methods for analysis of decision trees.
	library.wolfram.com /infocenter/Articles/1502 (146 words)

	Probabilistic Methods for Structure Computation and Display
		Currently, we are further testing this hypothesis with a combination of (1) basic investigation into the mathematical and implementational issues of computing with biological structure, and (2) collaborative applications to test the real-world utility of new representations and algorithms on problems of macromolecular structure.
		We are developing optimization methods that perform their search only in van der Waals-legal regions of space, so that all resulting structures satisfying specified packing distances.
		We have published work on the probabilistic algorithm which shows that it is relatively robust to local minima, and is able to converge to proper solutions given a wide range of input data.
	helix-web.stanford.edu /probmeth.html (1120 words)

	Probabilistic Reasoning in Decision Support Systems: From Computation to Common Sense
		With respect to the building of probabilistic models, the thesis argues on theoretical and empirical grounds, that it is essential to understand and explore the interaction between probability and causality.
		It demonstrates that the notion of causality in the recently proposed methods for construction of causal graphs from observations (Pearl 1991, Spirtes 1993) is almost identical with the notion of causality in econometric models (Simon 1953).
		The feasibility of automatic generation of explanations of probabilistic inference is demonstrated by developing the foundations for two methods of explaining probabilistic inference, one based on belief propagation and the other on scenario-based reasoning.
	www.pitt.edu /~druzdzel/abstracts/thesis.html (800 words)

	Probabilistic Design
		He has been heavily involved in the development and application of probabilistic methods for over 15 years and has applied probabilistic methods to geo-mechanics, biomechanics and other transient non-linear problems.
		He is an active member of the ASCE Technical Committee on Probabilistic Methods (Engineer Mechanics Division) and a regular reviewer for the ASCE Journal of Engineering Mechanics and several reliability-based journals.
		The safety factors in most modern engineering design codes have a probabilistic basis, such that the various sources of uncertainties are properly accounted for.However, in special or one-of-a-kind design situations that are not covered by the design code, these safety factors may not be applicable and a probabilistic design methodology is warranted.
	training.bossintl.com /html/probabilistic-design-training.html (851 words)

	CR: CS/0155 (sec 1) Probabilistic Methods in Computer Science (Site not responding. Last check: 2007-10-08)
		"Probabilistic Methods" stresses the importance of randomized approaches to solving otherwise complex problems, paying particular attention to its role in algorithm behavior.
		As somewhat expected, a few students objected to this method of teaching, claiming that it causes the instructor to move much too quickly and makes it near impossible to keep up with.
		Some also made mention that his use of the flboard had much to be desired but bear in mind that this came from a minority of respondents.
	www.brown.edu /Students/Critical_Review/2001.2002.2/CS0155_1UPF.html (398 words)

	Probabilistic Methods in Combinatorial Analysis - Cambridge University Press
		These methods not only provide the means of efficiently using such notions as characteristic and generating functions, the moment method and so on but also let us use the powerful technique of limit theorems.
		The basic objects under investigation are nonnegative matrices, partitions and mappings of finite sets, with special emphasis on permutations and graphs, and equivalence classes specified on sequences of finite length consisting of elements of partially ordered sets; these specify the probabilistic setting of Sachkov's general combinatorial scheme.
		The author pays special attention to using probabilistic methods to obtain asymptotic formulae that are difficult to derive using combinatorial methods.
	www.cambridge.org /catalogue/catalogue.asp?ISBN=052145512X (221 words)

	Hemanshu Kaul: Proposal for a Course on Topics in Probabilistic Methods
		The focus is on developing the themes underlying the various methods and illustrating the final results through applications in graph theory, combinatorial optimization and theoretical computer science.
		These methods, including coupling, conductance, and canonical paths, will be used in applications of the MCMC method to the Knapsack problem, proper colorings of a graph, linear extensions of a poset, permanent of a 0,1-matrix, etc.
		Jerrum, Mathematical foundations of the Markov chain Monte Carlo method, In Probabilistic Methods for Algorithmic Discrete Mathematics, (Springer, 1998), 116--165.
	www.math.uiuc.edu /~hkaul/MethodsCourseProposal.html (970 words)

	[No title]
		The history of the use of probabilistic methods goes back as far as the early sixties but for some reason the early ideas never took hold.
		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.
		One obvious method suggests itself: namely, to enlarge the initial request by using additional index terms which have a similar or related meaning to those of the given request'[4].
	www.dcs.gla.ac.uk /Keith/Chapter.6/Ch.6.html (11062 words)

	Wiley::The Probabilistic Method, 2nd Edition
		When it was first published in 1991, The Probabilistic Method became instantly the standard reference on one of the most powerful and widely used tools in combinatorics.
		The Probabilistic Method, Second Edition begins with basic techniques that use expectation and variance, as well as the more recent martingales and correlation inequalities, then explores areas where probabilistic techniques proved successful, including discrepancy and random graphs as well as cutting-edge topics in theoretical computer science.
		A series of proofs, or "probabilistic lenses," are interspersed throughout the book, offering added insight into the application of the probabilistic approach.
	www.wiley.com /WileyCDA/WileyTitle/productCd-0471370460.html (308 words)

	NE (NEEP) 574: Methods for Probabilistic Risk Analysis of Nuclear Power Plants
		Methods for risk and reliability analysis of engineered systems, particularly as applied in the nuclear power industry.
		introduce students to the methods actually used in industry for probabilistic risk analysis of nuclear power plants.
		NEEP 574 focuses on probabilistic methods for identifying weak components in a nuclear system, including the human component.
	www.engr.wisc.edu /ep/neep/courses/neep574.html (756 words)

	Randomized algorithm - Wikipedia, the free encyclopedia
		A randomized algorithm or probabilistic algorithm is an algorithm which employs a degree of randomness as part of its logic.
		Also refer to Probabilistic analysis, which is based on assuming something about the set of all possible inputs.
		J(D,k): (B,K) → R. When dealing with probabilistic methods for robustness, we assume that the uncertainty D is a random matrix distributed according to a probability density function f(D) with support B. Then, we formulate various problems.
	en.wikipedia.org /wiki/Probabilistic_algorithm (1762 words)

	ProFES Introduction - Probabilistic Mechanics
		The Probabilistic Finite Element System (ProFES) application is developed by the Computational Mechanics Group of ARA's Southeast Division, located in Raleigh NC.
		Probabilistic finite element analysis has advanced to the point that specialists can solve very complex problems.
		Computational methods for probabilistic mechanics are well developed and widely available, including FORM/SORM methods, static and adaptive response surface methods, simulation methods, and adaptive importance sampling.
	www.profes.com (367 words)

	Advanced Monte Carlo Methods
		Thus, Monte Carlo methods themselves are a fruitful source of research problems, and when combined with deterministic methods have the promise to provide many improved numerical methods.
		These methods can be used for various fundamental problems in numerical linear algebra as well as the solution of problems amenable to solution via Picard iteration.
		Of particular importance to students of Monte Carlo methods is that they discuss the solution of the difference approximations for elliptic and parabolic equations through probabilistic methods.
	www.cs.fsu.edu /~mascagni/Advanced_Monte_Carlo_Methods.html (2953 words)

	DIMACS Workshop on Probabilistic Methods in Discrete Mathematics (Site not responding. Last check: 2007-10-08)
		The Probabilistic Method was developed by Paul Erdos as a technique for proving the existence of a combinatorial object, coloring, tournament, graph or whatever, by proving that an appropriately defined random object has the desired properties with positive probability.
		Probabilistic techniques also played a major role in the most exciting development in Theoretical Computer Science over the last decade; the result that any NP statement has a (short) proof that can be checked probabilistically with extreme efficiently, and its relevance to the hardness of approximation of many interesting problems in Combinatorial Optimization.
		The study of Random Algorithms is closely aligned to the Probabilistic Method.
	dimacs.rutgers.edu /Workshops/Probabilistic/announcement.html (508 words)

	Food Safety Risk Analysis Training - Quantitative Risk Assessment Methods: Probabilistic Methods
		Quantitative risk assessment and quantitative methods generally can be very powerful, but require a strong command of the science and art of probabilistic methods.
		This short course trains aspiring and experienced modelers in the use of probabilistic methods.
		Basic Statistics: The quantitative methods courses do not require in-depth knowledge of statistics, but an understanding of basic terminology is necessary.
	www.jifsan.umd.edu /pd2006/courses_quantitative_ra_meth_prob.cfm (445 words)

	PROBABILISTIC METHODS IN FLUIDS
		This volume contains recent research papers presented at the international workshop on "Probabilistic Methods in Fluids" held in Swansea.
		The central problems considered were turbulence and the Navier—Stokes equations but, as is now well known, these classical problems are deeply intertwined with modern studies of stochastic partial differential equations, jump processes and random dynamical systems.
		The volume provides a snapshot of current studies in a field where the applications range from the design of aircraft through the mathematics of finance to the study of fluids in porous media.
	www.worldscibooks.com /mathematics/5156.html (220 words)

	Predictive and Probabilistic Technology Solutions - Veros Software (Site not responding. Last check: 2007-10-08)
		As the field of Probabilistic Methods has recently exploded, this annual event will allow for the dissemination of a wide variety of ideas regarding Probabilistic Methods.
		The PMC 2001 conference will be the first of its kind allowing engineers, analysts, software developers, program directors, and product designers to exchange ideas in the field of Probabilistic Methods.
		In addition this forum will demonstrate how Probabilistic Methods are being used for: cost reduction, designing for six sigma (DFSS), reliability prediction and improvement, risk/safety assessment, optimization, life extension, and weight savings.
	www.veros.com /vmenu.php?p=may142001 (270 words)

	Robotics Institute: Probabilistic Methods for Mobile Robot Mapping
		Over the last years, probabilistic methods have shown to be well suited for dealing with the uncertainties involved in mobile robot map building.
		In this paper we introduce a general probabilistic approach to concurrent mapping and localization.
		This method poses the mapping problem as a statistical maximum likelihood problem, and devises an efficient algorithm for search in likelihood space.
	www.ri.cmu.edu /pubs/pub_2662.html (264 words)

	SwRI Workshops
		This 4-1/2 day course is intended for engineers, scientists, and technical managers concerned with managing the uncertainties and risks of structural, mechanical, and other engineering systems, and desire to become familiar with the background and use of state-of-the-art probabilistic methods.
		The course focuses on the theoretical background, computational implementation, and application methods for probabilistic analysis and design.
		NESSUS probabilistic analysis computer program will be given to attendees at the completion of the course.
	www.swri.edu /psamsc/default.htm (250 words)

	CR: CS/0155 (sec 001) Probabilistic Methods in Computer Science (Site not responding. Last check: 2007-10-08)
		In “Probabilistic Methods in Computer Science” — better known as CS 155 — students learn the use of probabilistic logarithms in Computer Science.
		The course discusses randomized algorithms and teaches probability theory and methods.
		Students recommend that if you took CS152 and liked it, “Probabilistic Methods in Computer Science” is a class that you should definitely take.
	www.brown.edu /Students/Critical_Review/2002.2003.2/CS0155_1UPF.html (262 words)

	Probabilistic Methods in Structural Engineering (Site not responding. Last check: 2007-10-08)
		Papers are presented from a symposium on the application of probabilistic methods to structural engineering.
		In the second section, methods of risk analysis are reviewed for various types of structures including aircraft, dam, offshore, nuclear, and LNG terminal structures, and water supply systems.
		The third section deals with modern analytical methods in structural engineering including random vibration, crossing rates, partial factors, active control, and fatigue reliability.
	www.asce.org /bookstore/book.cfm?book=3444 (199 words)

	Compositional Methods for Probabilistic Systems
		The behavior of a system with probabilistic choice is a stochastic process, namely, a probability distribution on traces, or "bundle." Consequently, the semantics of a system with both nondeterministic and probabilistic choice is a set of bundles.
		The first mechanism, which is standard in compositional modeling, distinguishes inputs from outputs and hidden state.
		The second mechanism, which arises in probabilistic systems, partitions the state into probabilistically independent regions.
	www.gigascale.org /pubs/246.html (303 words)

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