MA4L3 Large Deviation Theory
Lecturer: Stefan Adams
Term(s): Term 2
Status for Mathematics students: List C
Commitment: 30 Lectures
Assessment: 85% Exam and 15% Homework
Formal registration prerequisites: None
Assumed knowledge:
 MA359 Measure Theory
 ST342 Mathematics of Random Events
 MA250 Introduction to Partial Differential Equations
Useful background:
 MA3H2 Markov Processes and Percolation Theory
 MA209 Variational Principles
 MA3G7 Functional Analysis I
 MA3G1 Theory of PDEs
Synergies:
Content:
 Basic understanding of large deviation techniques (definition, basic properties, Cramer’s theorem, Varadhan’s lemma, Sanov’s theorem, the GärtnerEllis Theorem).
 Large deviation approach to Gibbs measure theory (free energy; entropy; variational analysis; empirical process; mathematics of phase transition).
 Large deviation theory for stochastic processes and its connections with PDEs (Fleming semi group; viscosity solutions; control theory).
 Applications of large deviation theory (at least one of the following list of topics: interface models; pinning/wetting models; dynamical systems; decay of connectivity in percolation; Gaussian Free Field; Free energy calculations; Wasserstein gradient flow; renormalisation theory (multiscale analysis)).
Aims:
 Basic understanding of large deviation theory (rate function; free energy; entropy; Legendretransform).
 Understanding that large deviation principles provide a bridge between probability and analysis (PDEs, convex and variational analysis).
 Large deviation theory as the mathematical foundation of mathematical statistical mechanics (Gibbs measures; free energy calculations; entropyenergy competition).
 Understanding large deviation in terms of the nonlinear Fleming semi group and its links to control theory.
 Discussion of the role of large deviation methods and results in joining different scales, e.g. as the micromacro passage in interacting systems.
 Connection of large deviation theory with stochastic limit theorems (law of large numbers; ergodic theorems (time and space translations); scaling limits).
Objectives: By the end of the module students should be able to:
 Derive basic large deviation principles
 Be familiar with the variational principle and the large deviation approach to Gibbs measure
 Distinguish all three level of large deviation
 To calculate LegendreFenchel transform for most relevant distributions
 Understand basic variational problems
 Be familiar with some application of large deviation theory
 Link basic large deviation principle for stochastic processes to PDEs
 Compute of rare probabilities via large deviation rate functions given as variational problems in analysis and PDE theory. Be able to use Legendretransform techniques, basic convex analysis and Laplace integral methods.

Understand the role of free energy calculations and representations in analysis (PDEs and control problems and variational problems). Be able to provide a variational description of Gibbs measures.

Be able to analyse the minimiser of large deviation rate functions of basic examples and to provide interpretation of the possible occurrence of multiple minimiser.

Explain the role of the free energy in interacting systems and its link to stochastic modelling. Be able to provide different representations of the free energy for some basic examples.

Be able to estimate probabilities for interacting systems using Laplace integral techniques and basic understanding of Gibbs distributions.

Apply large deviation theory to one topic from the following list: interface models; pinning/wetting models (random walk models); dynamical systems; decay of connectivity in percolation; Gaussian Free Field; Free energy calculations; Wasserstein gradient flow; renormalisation theory (multiscale analysis).
Books: We won’t follow a particular book and will provide lecture notes. The course is based on the following three books:
[1] Frank den Hollander, Large Deviations (Fields Institute Monographs), (paperback), American Mathematical Society (2008).
[2] Amir Dembo & Ofer Zeitouni, Large Deviations Techniques and Applications (Stochastic Modelling and Applied Probability), (paperback), Springer (2009).
[3] Jin Feng and Thomas G. Kurtz, Large Deviations for Stochastic Processes, American Mathematical Society (2006).
Other relevant books and lecture notes:
[a] HansOtto Georgii, Gibbs Measures and Phase Transitions, De Gruyter (1988).
[b] Stefan Adams, Lectures on mathematical statistical mechanics, Communications of the Dublin Institute for Advanced Studies Series A (Theoretical Physics), No. 30 , available online http://www2.warwick.ac.uk/fac/sci/maths/people/staff/stefan adams/lecturenotestvi/cdiasadams30.pdf
[c] Stefan Adams, Large Deviations for Stochastic Processes, EURANDOM reports 201225, (2012); available online http://www.eurandom.tue.nl/reports/2012/025report.pdf