www.mathtube.org - Probability
http://www.mathtube.org/taxonomy/term/120/0
enDepth Functions in Multivariate & Other Data Settings: Concepts, Perspectives, Tools, & Applications
http://www.mathtube.org/lecture/video/depth-functions-multivariate-other-data-settings-concepts-perspectives-tools-applicati
<div class="field field-type-text field-field-speaker">
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Speaker: </div>
Robert Serfling </div>
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Date: </div>
<span class="date-display-single">Thu, Sep 28, 2017</span> </div>
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Location: </div>
PIMS, University of Manitoba </div>
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</div>
<div class="field field-type-text field-field-conference">
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Conference: </div>
PIMS-UManitoba Distinguished Lecture </div>
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<div class="field field-type-text field-field-abstract">
<div class="field-label">Abstract: </div>
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<div class="field-item odd">
Depth functions were developed to extend the univariate notions of median, quantiles, ranks, signs, and order statistics to the setting of multivariate data. Whereas a probability density function measures local probability weight, a depth function measures centrality. The contours of a multivariate depth function induce closely associated multivariate outlyingness, quantile, sign, and rank functions. Together, these functions comprise a powerful methodology for nonparametric multivariate data description, outlier detection, data analysis, and inference, including for example location and scatter estimation, tests of symmetry, and multivariate boxplots. Due to the lack of a natural order in dimension higher than 1, notions such as median and quantile are not uniquely defined, however, posing a challenging conceptual arena. How to define the middle? The middle half? Interesting competing formulations of depth functions in the multivariate setting have evolved, and extensions to functional data in Hilbert space have been developed and more recently, to multivariate functional data. A key question is how generally a notion of depth function can be productively defined. This talk provides a perspective on depth, outlyingness, quantile, and rank functions, through an overview coherently treating concepts, roles, key properties, interrelations, data settings, applications, open issues, and new potentials. </div>
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ScientificMathematicsStatistics TheoryProbabilityMon, 11 Dec 2017 21:55:11 +0000root686 at http://www.mathtube.orgRandom Maps 10
http://www.mathtube.org/lecture/video/random-maps-10
<div class="field field-type-text field-field-speaker">
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<div class="field-item odd">
<div class="field-label-inline-first">
Speaker: </div>
Gregory Miermont </div>
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</div>
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Date: </div>
<span class="date-display-single">Tue, Jun 19, 2012</span> </div>
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</div>
<div class="field field-type-text field-field-location">
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<div class="field-item odd">
<div class="field-label-inline-first">
Location: </div>
PIMS, University of British Columbia </div>
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</div>
<div class="field field-type-text field-field-conference">
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<div class="field-label-inline-first">
Conference: </div>
PIMS-MPrime Summer School in Probability </div>
</div>
</div>
<div class="field field-type-text field-field-abstract">
<div class="field-label">Abstract: </div>
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<div class="field-item odd">
N.B. Due to a problem with the microphone, the audio for this recording is almost entirely missing. It is displayed here in the hope that the whiteboard material is still useful.
The study of maps, that is of graphs embedded in surfaces, is a popular subject that has implications in many branches of mathematics, the most famous aspects being purely graph-theoretical, such as the four-color theorem. The study of random maps has met an increasing interest in the recent years. This is motivated in particular by problems in theoretical physics, in which random maps serve as discrete models of random continuum surfaces. The probabilistic interpretation of bijective counting methods for maps happen to be particularly fruitful, and relates random maps to other important combinatorial random structures like the continuum random tree and the Brownian snake. This course will survey these aspects and present recent developments in this area. </div>
</div>
</div>
ScientificMathematicsProbabilityFri, 24 Nov 2017 23:37:18 +0000root684 at http://www.mathtube.orgRandom Maps 2
http://www.mathtube.org/lecture/video/random-maps-2-0
<div class="field field-type-text field-field-speaker">
<div class="field-items">
<div class="field-item odd">
<div class="field-label-inline-first">
Speaker: </div>
Gregory Miermont </div>
</div>
</div>
<div class="field field-type-datetime field-field-date">
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<div class="field-item odd">
<div class="field-label-inline-first">
Date: </div>
<span class="date-display-single">Tue, Jun 5, 2012</span> </div>
</div>
</div>
<div class="field field-type-text field-field-location">
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<div class="field-item odd">
<div class="field-label-inline-first">
Location: </div>
PIMS, University of British Columbia </div>
</div>
</div>
<div class="field field-type-text field-field-conference">
<div class="field-items">
<div class="field-item odd">
<div class="field-label-inline-first">
Conference: </div>
PIMS-MPrime Summer School in Probability </div>
</div>
</div>
<div class="field field-type-text field-field-abstract">
<div class="field-label">Abstract: </div>
<div class="field-items">
<div class="field-item odd">
The study of maps, that is of graphs embedded in surfaces, is a popular subject that has implications in many branches of mathematics, the most famous aspects being purely graph-theoretical, such as the four-color theorem. The study of random maps has met an increasing interest in the recent years. This is motivated in particular by problems in theoretical physics, in which random maps serve as discrete models of random continuum surfaces. The probabilistic interpretation of bijective counting methods for maps happen to be particularly fruitful, and relates random maps to other important combinatorial random structures like the continuum random tree and the Brownian snake. This course will survey these aspects and present recent developments in this area. </div>
</div>
</div>
ScientificMathematicsProbabilityThu, 23 Nov 2017 23:54:35 +0000root683 at http://www.mathtube.orgExtrema of 2D Discrete Gaussian Free Field - Lecture 16
http://www.mathtube.org/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-16
<div class="field field-type-text field-field-speaker">
<div class="field-items">
<div class="field-item odd">
<div class="field-label-inline-first">
Speaker: </div>
Marek Biskup </div>
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</div>
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Date: </div>
<span class="date-display-single">Sun, Jul 30, 2017</span> </div>
</div>
</div>
<div class="field field-type-text field-field-location">
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<div class="field-item odd">
<div class="field-label-inline-first">
Location: </div>
PIMS, University of British Columbia </div>
</div>
</div>
<div class="field field-type-text field-field-conference">
<div class="field-items">
<div class="field-item odd">
<div class="field-label-inline-first">
Conference: </div>
PIMS-CRM Summer School in Probability 2017 </div>
</div>
</div>
<div class="field field-type-text field-field-abstract">
<div class="field-label">Abstract: </div>
<div class="field-items">
<div class="field-item odd">
The Gaussian free field (GFF) is a fundamental model for random fluctuations of a surface. The GFF is closely related to local times of random walks via relations that originated in the study of spin systems. The continuous GFF appears as the limit law of height functions of dimer covers, uniform spanning trees and other models without apparent Gaussian correlation structure. The GFF is also a simple example of a quantum field theory. Intriguing connections to SLE, the Brownian map and other recently studied problems exist. The GFF has recently become subject of focused interest by probabilists. Through Kahane's theory of multiplicative chaos, the GFF naturally enters into models of Liouville quantum gravity. Multiplicative chaos is also central to the description of level sets where the GFF takes values proportional to its maximum, or values order-unity away from the absolute maximum. Random walks in random environments given as exponentials of the GFF show intriguing subdiffusive behavior. Universality of these conclusions for other models such as gradient systems and/or local times of random walks are within reach.
<p> </p>
<ul>
<li><b>Lectures in this course</b>: <a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-1">1</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-2">2</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-3">3</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-4">4</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-5">5</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-6">6</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-7">7</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-8">8</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-9">9</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-10">10</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-11">11</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-12">12</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-13">13</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-14">14</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-15">15</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-16">16</a>
</li><li><a href="https://www.math.ucla.edu/~biskup/PIMS/plan.html">Notes</a></li>
</ul> </div>
</div>
</div>
ScientificMathematicsProbabilitySun, 02 Jul 2017 07:55:54 +0000root669 at http://www.mathtube.orgGraphical approach to lattice spin models - Lecture 16
http://www.mathtube.org/lecture/video/graphical-approach-lattice-spin-models-lecture-16
<div class="field field-type-text field-field-speaker">
<div class="field-items">
<div class="field-item odd">
<div class="field-label-inline-first">
Speaker: </div>
Hugo Duminil-Copin </div>
</div>
</div>
<div class="field field-type-datetime field-field-date">
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<div class="field-item odd">
<div class="field-label-inline-first">
Date: </div>
<span class="date-display-single">Fri, Jun 30, 2017</span> </div>
</div>
</div>
<div class="field field-type-text field-field-location">
<div class="field-items">
<div class="field-item odd">
<div class="field-label-inline-first">
Location: </div>
PIMS, University of British Columbia </div>
</div>
</div>
<div class="field field-type-text field-field-conference">
<div class="field-items">
<div class="field-item odd">
<div class="field-label-inline-first">
Conference: </div>
PIMS-CRM Summer School in Probability 2017 </div>
</div>
</div>
<div class="field field-type-text field-field-abstract">
<div class="field-label">Abstract: </div>
<div class="field-items">
<div class="field-item odd">
Phase transitions are a central theme of statistical mechanics, and of probability more generally. Lattice spin models represent a general paradigm for phase transitions in finite dimensions, describing ferromagnets and even some fluids (lattice gases). It has been understood since the 1980s that random geometric representations, such as the random walk and random current representations, are powerful tools to understand spin models. In addition to techniques intrinsic to spin models, such representations provide access to rich ideas from percolation theory. In recent years, for two-dimensional spin models, these ideas have been further combined with ideas from discrete complex analysis. Spectacular results obtained through these connections include the proof that interfaces of the two-dimensional Ising model have conformally invariant scaling limits given by SLE curves, that the connective constant of the self-avoiding walk on the hexagonal lattice is given by \u221a 2 + \u221a 2 , and that the magnetisation of the three-dimensional Ising model vanishes at the critical point.
<p> </p>
<ul>
<li><b>Lectures in this course</b>: <a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-1">1</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-2">2</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-3">3</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-4">4</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-5">5</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-6">6</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-7">7</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-8">8</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-9">9</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-10">10</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-11">11</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-12">12</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-13">13</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-14">14</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-15">15</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-16">16</a></li>
<li><a href="https://www.math.ucla.edu/~biskup/PIMS/plan.html">Notes</a></li>
</ul> </div>
</div>
</div>
ScientificMathematicsProbabilitySat, 01 Jul 2017 02:58:16 +0000root668 at http://www.mathtube.orgGraphical approach to lattice spin models - Lecture 15
http://www.mathtube.org/lecture/video/graphical-approach-lattice-spin-models-lecture-15
<div class="field field-type-text field-field-speaker">
<div class="field-items">
<div class="field-item odd">
<div class="field-label-inline-first">
Speaker: </div>
Hugo Duminil-Copin </div>
</div>
</div>
<div class="field field-type-datetime field-field-date">
<div class="field-items">
<div class="field-item odd">
<div class="field-label-inline-first">
Date: </div>
<span class="date-display-single">Thu, Jun 29, 2017</span> </div>
</div>
</div>
<div class="field field-type-text field-field-location">
<div class="field-items">
<div class="field-item odd">
<div class="field-label-inline-first">
Location: </div>
PIMS, University of British Columbia </div>
</div>
</div>
<div class="field field-type-text field-field-conference">
<div class="field-items">
<div class="field-item odd">
<div class="field-label-inline-first">
Conference: </div>
PIMS-CRM Summer School in Probability 2017 </div>
</div>
</div>
<div class="field field-type-text field-field-abstract">
<div class="field-label">Abstract: </div>
<div class="field-items">
<div class="field-item odd">
Phase transitions are a central theme of statistical mechanics, and of probability more generally. Lattice spin models represent a general paradigm for phase transitions in finite dimensions, describing ferromagnets and even some fluids (lattice gases). It has been understood since the 1980s that random geometric representations, such as the random walk and random current representations, are powerful tools to understand spin models. In addition to techniques intrinsic to spin models, such representations provide access to rich ideas from percolation theory. In recent years, for two-dimensional spin models, these ideas have been further combined with ideas from discrete complex analysis. Spectacular results obtained through these connections include the proof that interfaces of the two-dimensional Ising model have conformally invariant scaling limits given by SLE curves, that the connective constant of the self-avoiding walk on the hexagonal lattice is given by \u221a 2 + \u221a 2 , and that the magnetisation of the three-dimensional Ising model vanishes at the critical point.
<p> </p>
<ul>
<li><b>Lectures in this course</b>: <a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-1">1</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-2">2</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-3">3</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-4">4</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-5">5</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-6">6</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-7">7</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-8">8</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-9">9</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-10">10</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-11">11</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-12">12</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-13">13</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-14">14</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-15">15</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-16">16</a></li>
<li><a href="https://www.math.ucla.edu/~biskup/PIMS/plan.html">Notes</a></li>
</ul> </div>
</div>
</div>
ScientificMathematicsProbabilityFri, 30 Jun 2017 04:17:30 +0000root667 at http://www.mathtube.orgExtrema of 2D Discrete Gaussian Free Field - Lecture 15
http://www.mathtube.org/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-15
<div class="field field-type-text field-field-speaker">
<div class="field-items">
<div class="field-item odd">
<div class="field-label-inline-first">
Speaker: </div>
Marek Biskup </div>
</div>
</div>
<div class="field field-type-datetime field-field-date">
<div class="field-items">
<div class="field-item odd">
<div class="field-label-inline-first">
Date: </div>
<span class="date-display-single">Thu, Jun 29, 2017</span> </div>
</div>
</div>
<div class="field field-type-text field-field-location">
<div class="field-items">
<div class="field-item odd">
<div class="field-label-inline-first">
Location: </div>
PIMS, University of British Columbia </div>
</div>
</div>
<div class="field field-type-text field-field-conference">
<div class="field-items">
<div class="field-item odd">
<div class="field-label-inline-first">
Conference: </div>
PIMS-CRM Summer School in Probability 2017 </div>
</div>
</div>
<div class="field field-type-text field-field-abstract">
<div class="field-label">Abstract: </div>
<div class="field-items">
<div class="field-item odd">
The Gaussian free field (GFF) is a fundamental model for random fluctuations of a surface. The GFF is closely related to local times of random walks via relations that originated in the study of spin systems. The continuous GFF appears as the limit law of height functions of dimer covers, uniform spanning trees and other models without apparent Gaussian correlation structure. The GFF is also a simple example of a quantum field theory. Intriguing connections to SLE, the Brownian map and other recently studied problems exist. The GFF has recently become subject of focused interest by probabilists. Through Kahane's theory of multiplicative chaos, the GFF naturally enters into models of Liouville quantum gravity. Multiplicative chaos is also central to the description of level sets where the GFF takes values proportional to its maximum, or values order-unity away from the absolute maximum. Random walks in random environments given as exponentials of the GFF show intriguing subdiffusive behavior. Universality of these conclusions for other models such as gradient systems and/or local times of random walks are within reach.
<p> </p>
<ul>
<li><b>Lectures in this course</b>: <a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-1">1</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-2">2</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-3">3</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-4">4</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-5">5</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-6">6</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-7">7</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-8">8</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-9">9</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-10">10</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-11">11</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-12">12</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-13">13</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-14">14</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-15">15</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-16">16</a>
</li><li><a href="https://www.math.ucla.edu/~biskup/PIMS/plan.html">Notes</a></li>
</ul> </div>
</div>
</div>
ScientificMathematicsProbabilityFri, 30 Jun 2017 01:47:08 +0000root666 at http://www.mathtube.orgGraphical approach to lattice spin models - Lecture 14
http://www.mathtube.org/lecture/video/graphical-approach-lattice-spin-models-lecture-14
<div class="field field-type-text field-field-speaker">
<div class="field-items">
<div class="field-item odd">
<div class="field-label-inline-first">
Speaker: </div>
Hugo Duminil-Copin </div>
</div>
</div>
<div class="field field-type-datetime field-field-date">
<div class="field-items">
<div class="field-item odd">
<div class="field-label-inline-first">
Date: </div>
<span class="date-display-single">Tue, Jun 27, 2017</span> </div>
</div>
</div>
<div class="field field-type-text field-field-location">
<div class="field-items">
<div class="field-item odd">
<div class="field-label-inline-first">
Location: </div>
PIMS, University of British Columbia </div>
</div>
</div>
<div class="field field-type-text field-field-conference">
<div class="field-items">
<div class="field-item odd">
<div class="field-label-inline-first">
Conference: </div>
PIMS-CRM Summer School in Probability 2017 </div>
</div>
</div>
<div class="field field-type-text field-field-abstract">
<div class="field-label">Abstract: </div>
<div class="field-items">
<div class="field-item odd">
Phase transitions are a central theme of statistical mechanics, and of probability more generally. Lattice spin models represent a general paradigm for phase transitions in finite dimensions, describing ferromagnets and even some fluids (lattice gases). It has been understood since the 1980s that random geometric representations, such as the random walk and random current representations, are powerful tools to understand spin models. In addition to techniques intrinsic to spin models, such representations provide access to rich ideas from percolation theory. In recent years, for two-dimensional spin models, these ideas have been further combined with ideas from discrete complex analysis. Spectacular results obtained through these connections include the proof that interfaces of the two-dimensional Ising model have conformally invariant scaling limits given by SLE curves, that the connective constant of the self-avoiding walk on the hexagonal lattice is given by √ 2 + √ 2 , and that the magnetisation of the three-dimensional Ising model vanishes at the critical point.
<p> </p>
<ul>
<li><b>Lectures in this course</b>: <a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-1">1</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-2">2</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-3">3</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-4">4</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-5">5</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-6">6</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-7">7</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-8">8</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-9">9</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-10">10</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-11">11</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-12">12</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-13">13</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-14">14</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-15">15</a>,
<a href="/lecture/video/graphical-approach-lattice-spin-models-lecture-16">16</a></li>
<li><a href="https://www.math.ucla.edu/~biskup/PIMS/plan.html">Notes</a></li>
</ul> </div>
</div>
</div>
ScientificMathematicsProbabilityWed, 28 Jun 2017 02:20:35 +0000root665 at http://www.mathtube.orgExtrema of 2D Discrete Gaussian Free Field - Lecture 14
http://www.mathtube.org/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-14
<div class="field field-type-text field-field-speaker">
<div class="field-items">
<div class="field-item odd">
<div class="field-label-inline-first">
Speaker: </div>
Marek Biskup </div>
</div>
</div>
<div class="field field-type-datetime field-field-date">
<div class="field-items">
<div class="field-item odd">
<div class="field-label-inline-first">
Date: </div>
<span class="date-display-single">Tue, Jun 27, 2017</span> </div>
</div>
</div>
<div class="field field-type-text field-field-location">
<div class="field-items">
<div class="field-item odd">
<div class="field-label-inline-first">
Location: </div>
PIMS, University of British Columbia </div>
</div>
</div>
<div class="field field-type-text field-field-conference">
<div class="field-items">
<div class="field-item odd">
<div class="field-label-inline-first">
Conference: </div>
PIMS-CRM Summer School in Probability 2017 </div>
</div>
</div>
<div class="field field-type-text field-field-abstract">
<div class="field-label">Abstract: </div>
<div class="field-items">
<div class="field-item odd">
The Gaussian free field (GFF) is a fundamental model for random fluctuations of a surface. The GFF is closely related to local times of random walks via relations that originated in the study of spin systems. The continuous GFF appears as the limit law of height functions of dimer covers, uniform spanning trees and other models without apparent Gaussian correlation structure. The GFF is also a simple example of a quantum field theory. Intriguing connections to SLE, the Brownian map and other recently studied problems exist. The GFF has recently become subject of focused interest by probabilists. Through Kahane's theory of multiplicative chaos, the GFF naturally enters into models of Liouville quantum gravity. Multiplicative chaos is also central to the description of level sets where the GFF takes values proportional to its maximum, or values order-unity away from the absolute maximum. Random walks in random environments given as exponentials of the GFF show intriguing subdiffusive behavior. Universality of these conclusions for other models such as gradient systems and/or local times of random walks are within reach.
<p> </p>
<ul>
<li><b>Lectures in this course</b>: <a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-1">1</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-2">2</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-3">3</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-4">4</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-5">5</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-6">6</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-7">7</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-8">8</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-9">9</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-10">10</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-11">11</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-12">12</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-13">13</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-14">14</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-15">15</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-16">16</a>
</li><li><a href="https://www.math.ucla.edu/~biskup/PIMS/plan.html">Notes</a></li>
</ul> </div>
</div>
</div>
ScientificMathematicsProbabilityTue, 27 Jun 2017 23:43:53 +0000root664 at http://www.mathtube.orgExtrema of 2D Discrete Gaussian Free Field - Lecture 13
http://www.mathtube.org/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-13
<div class="field field-type-text field-field-speaker">
<div class="field-items">
<div class="field-item odd">
<div class="field-label-inline-first">
Speaker: </div>
Marek Biskup </div>
</div>
</div>
<div class="field field-type-datetime field-field-date">
<div class="field-items">
<div class="field-item odd">
<div class="field-label-inline-first">
Date: </div>
<span class="date-display-single">Mon, Jun 26, 2017</span> </div>
</div>
</div>
<div class="field field-type-text field-field-location">
<div class="field-items">
<div class="field-item odd">
<div class="field-label-inline-first">
Location: </div>
PIMS, University of British Columbia </div>
</div>
</div>
<div class="field field-type-text field-field-conference">
<div class="field-items">
<div class="field-item odd">
<div class="field-label-inline-first">
Conference: </div>
PIMS-CRM Summer School in Probability 2017 </div>
</div>
</div>
<div class="field field-type-text field-field-abstract">
<div class="field-label">Abstract: </div>
<div class="field-items">
<div class="field-item odd">
The Gaussian free field (GFF) is a fundamental model for random fluctuations of a surface. The GFF is closely related to local times of random walks via relations that originated in the study of spin systems. The continuous GFF appears as the limit law of height functions of dimer covers, uniform spanning trees and other models without apparent Gaussian correlation structure. The GFF is also a simple example of a quantum field theory. Intriguing connections to SLE, the Brownian map and other recently studied problems exist. The GFF has recently become subject of focused interest by probabilists. Through Kahane's theory of multiplicative chaos, the GFF naturally enters into models of Liouville quantum gravity. Multiplicative chaos is also central to the description of level sets where the GFF takes values proportional to its maximum, or values order-unity away from the absolute maximum. Random walks in random environments given as exponentials of the GFF show intriguing subdiffusive behavior. Universality of these conclusions for other models such as gradient systems and/or local times of random walks are within reach.
<p> </p>
<ul>
<li><b>Lectures in this course</b>: <a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-1">1</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-2">2</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-3">3</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-4">4</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-5">5</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-6">6</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-7">7</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-8">8</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-9">9</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-10">10</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-11">11</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-12">12</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-13">13</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-14">14</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-15">15</a>,
<a href="/lecture/video/extrema-2d-discrete-gaussian-free-field-lecture-16">16</a>
</li><li><a href="https://www.math.ucla.edu/~biskup/PIMS/plan.html">Notes</a></li>
</ul> </div>
</div>
</div>
ScientificMathematicsProbabilityTue, 27 Jun 2017 03:25:48 +0000root663 at http://www.mathtube.org