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Workshop overview

A graphical model, or Bayesian network, encodes probabilistic relationships among variables. Techniques based on these models arebecoming increasingly important in data analysis applications of many types. In areas such as foreign-language translation, microchipmanufacturing, and drug discovery, the volume of data can slow progress because of the difficulty of finding causal connections ordependencies. The new Bayesian methods enable these tangled interconnections to be sorted out and produce useful tools forhandling large data sets. Google is already using these techniques to find and take advantage of patterns of interconnections between Webpages, and Bill Gates has been quoted as saying that expertise in Bayesian networks is an essential part of Microsoft's competitiveadvantage, particularly in such areas as speech recognition. (Bayesian networks now pervade Microsoft Office.) Recently, the MIT TechnologyReview named Bayesian networks as one of the top ten emerging technologies.

Remark: This workshop was held on February 19, 2004 as part of the Computational Sciences Lecture Series (CSLS) at the University of Wisconsin-Madison.

An introduction to probabilistic graphical models and their lyapunov functions and algorithms for inference and learning

By Prof. Brendan J. Frey (Probabilistic and Statistical Inference Group, Electrical and Computer Engineering,University of Toronto, Canada)

Slides of talk in PDF | Video [WMV]

ABSTRACT: Many problems in science and engineering require that we take into account uncertainties in the observed data and uncertaintiesin the model that is used to analyze the data. Probability theory (in particular, Bayes rule) provides a way to account for uncertainty, bycombining the evidence provided by the data with prior knowledge about the problem. Recently, we have seen an increasing abundance of dataand computational power, and this has motivated researchers to develop techniques for solving large-scale problems that require complexchains of reasoning applied to large datasets. For example, a typical problem that my group works on will have 100,000 to 1,000,000 or moreunobserved random variables. In such large-scale systems, the structure of the probability model plays a crucial role and thisstructure can be easily represented using a graph. In this talk, I will review the definitions and properties of the main types ofgraphical model, and the Lyapunov functions and optimization algorithms that can be used to perform inference and learning in thesemodels. Throughout the talk, I will use a simple example taken from the application area of computer vision, to demonstrate the concepts.

Graphical models for linear systems, codes and networks

By Prof. Ralf Koetter (Coordinated Science Laboratory and Department of Electrical Engineering,University of Illinois, Urbana-Champaign, USA)

Slides of talk in PDF | Video [WMV]

ABSTRACT: The use of graphical models of sytems is a well establishedtechnique to characterize a represented behavior. While these models are often given by nature in some cases it is possible to choose theunderlying graphical framework. If in addition the represented behavior satisfies certain linearity requirements, surprisingstructural properties of the underlying graphical models can be derived. We give an overview over a developing structure theory forlinear systems in graphical models and point out numerous directions for further research. Examples of applications of this theory aregiven that cover areas as different as coding, state space models and network information theory.

Graphical models, exponential families and variational inference

By Prof. Michael I. Jordan (Department of Computer Science, University of California Berkeley,USA)

Slides of talk in PDF | Video [WMV]

ABSTRACT: The formalism of probabilistic graphical models provides a unifying framework for the development of large-scale multivariatestatistical models. Graphical models have become a focus of researchin many applied statistical and computational fields, including bioinformatics, information theory, signal and image processing,information retrieval and machine learning. Many problems that arise in specific instances---including the key problems of computingmarginals and modes of probability distributions---are best studied in the general setting. Exploiting the conjugate duality between thecumulant generating funciton and the entropy for exponential families, we develop general variational representations of the problems ofcomputing marginals and modes. We describe how a wide variety of known computational algorithms---including mean field, sum-product andcluster variational techniques---can be understand in terms of these variational representations. We also present novel convex relaxationsbased on the variational framework. We present applications to problems in bioinformatics and information retrieval. [Joint work withMartin Wainwright]

Questions & Answers

Is there any normative that regulates the use of silver nanoparticles?
Damian Reply
what king of growth are you checking .?
What fields keep nano created devices from performing or assimulating ? Magnetic fields ? Are do they assimilate ?
Stoney Reply
why we need to study biomolecules, molecular biology in nanotechnology?
Adin Reply
yes I'm doing my masters in nanotechnology, we are being studying all these domains as well..
what school?
biomolecules are e building blocks of every organics and inorganic materials.
anyone know any internet site where one can find nanotechnology papers?
Damian Reply
sciencedirect big data base
Introduction about quantum dots in nanotechnology
Praveena Reply
what does nano mean?
Anassong Reply
nano basically means 10^(-9). nanometer is a unit to measure length.
do you think it's worthwhile in the long term to study the effects and possibilities of nanotechnology on viral treatment?
Damian Reply
absolutely yes
how to know photocatalytic properties of tio2 nanoparticles...what to do now
Akash Reply
it is a goid question and i want to know the answer as well
characteristics of micro business
for teaching engĺish at school how nano technology help us
Do somebody tell me a best nano engineering book for beginners?
s. Reply
there is no specific books for beginners but there is book called principle of nanotechnology
what is fullerene does it is used to make bukky balls
Devang Reply
are you nano engineer ?
fullerene is a bucky ball aka Carbon 60 molecule. It was name by the architect Fuller. He design the geodesic dome. it resembles a soccer ball.
what is the actual application of fullerenes nowadays?
That is a great question Damian. best way to answer that question is to Google it. there are hundreds of applications for buck minister fullerenes, from medical to aerospace. you can also find plenty of research papers that will give you great detail on the potential applications of fullerenes.
what is the Synthesis, properties,and applications of carbon nano chemistry
Abhijith Reply
Mostly, they use nano carbon for electronics and for materials to be strengthened.
is Bucky paper clear?
carbon nanotubes has various application in fuel cells membrane, current research on cancer drug,and in electronics MEMS and NEMS etc
so some one know about replacing silicon atom with phosphorous in semiconductors device?
s. Reply
Yeah, it is a pain to say the least. You basically have to heat the substarte up to around 1000 degrees celcius then pass phosphene gas over top of it, which is explosive and toxic by the way, under very low pressure.
Do you know which machine is used to that process?
how to fabricate graphene ink ?
for screen printed electrodes ?
What is lattice structure?
s. Reply
of graphene you mean?
or in general
in general
Graphene has a hexagonal structure
On having this app for quite a bit time, Haven't realised there's a chat room in it.
what is biological synthesis of nanoparticles
Sanket Reply
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