Concept

Markov random field

Related lectures (26)

Lindblad equation

Covers the interpretation of the Lindblad equation and its unitary part in quantum gases.

Stochastic Analysis & Random Fields

Covers the outcomes of a virtual seminar on stochastic analysis.

Lindblad equation

Covers the derivation of the Lindblad equation and the evolution of quantum gases.

Random Field Ising Model

Covers the Random Field Ising Model, discussing the concept of disorder and the process of finding the ground state.

Stochastic Games on Directed Networks

Covers linear-quadratic stochastic differential games on directed chain networks and open-loop Nash equilibria.

Spatial Ergodicity for SPDEs

Explores spatial ergodicity for SPDEs, covering basic formulations, initial data effects, and results on ergodicity and CLT.

Random Field Ising Model: Overview and Analysis

Provides an in-depth analysis of the Random Field Ising Model, covering model description, free entropy, and mean field algorithm.

Graph metrics: Statistical analysis

Explores graph metrics and statistical analysis in network clustering, including ERGMs application in sociology and asymptotics.

Machine Learning for Solving PDEs: Random Feature Method

Explores the Random Feature Method for solving PDEs using machine learning algorithms to approximate high-dimensional functions efficiently.

Virtual Seminar on Stochastic Analysis

Introduces a virtual seminar on stochastic analysis, covering a wide range of topics and focusing on the mathematical challenges of the COVID-19 crisis.

Belief Propagation on Graphs

Covers belief propagation on graphs, exploring computation challenges and heuristics, focusing on sparse random graphs' loop properties.

Debiased Whittle Likelihood: Time Series and Spatial Data

Explores the Debiased Whittle likelihood for time series and spatial data, focusing on fitting spectral density to the periodogram for better predictions and parameter estimation.

Martingales and Brownian Motion: Reflected and Absorbed Brownian Motion

Explores the reflected and absorbed Brownian motion transformations and Markov properties.

Latent Space Models: Inference and Applications

Explores latent space models, network representations, spectral decompositions, and parameter estimation methods.

Conditional Gaussian Generation

Explores the generation of multivariate Gaussian distributions and the challenges of factorizing covariance matrices.

Gaussian Acyclic Models: Linearity and Identifiability

Covers Gaussian Acyclic Models focusing on linearity and identifiability.

Thermodynamic Formalism for Dispersing Billiards

Explores the thermodynamic formalism for dispersing billiards, covering topological entropy, weight functions, and fragmentation lemmas.

Maximum of Gaussians

Explores the p-spin model in spin glass theory and the convergence to the Random Energy Model using Gaussian integrals and the replica method.

Lindblad Equation

Explores the Lindblad equation and the Markov approximation in quantum optics.

Network Inference from Textual Evidence

Delves into network inference from textual evidence, exploring information propagation, translation, and multi-input attention.