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Stochastic modelling for statistical applications
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Stochastic modelling for statistical applications
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Academic year 2020/2021
- Course ID
- MAT0039
- Teaching staff
- Matteo Ruggiero
Paul Anthony Jenkins (Lecturer) - Year
- 1st year
- Teaching period
- Second semester
- Type
- D.M. 270 TAF B - Distinctive
- Credits/Recognition
- 6
- Course disciplinary sector (SSD)
- MAT/06 - probabilita' e statistica matematica
- Delivery
- Class Lectures
- Language
- English
- Attendance
- Optional
- Type of examination
- Oral
- Prerequisites
- PROBABILITY THEORY (MAT0034)
- Propedeutic for
- BAYESIAN STATISTICS (MAT0070)
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Sommario del corso
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Course objectives
The course introduces to the theory of Markov chains, in discrete and continuous time, and Lévy processes, considered essential probabilistic instruments which should be part of a modern statistician's toolbox. As an illustrative application, some time will be devoted to introduce the basics of Markov chain Monte Carlo simulation methods, with a few examples of the most widely used algorithms.
A module of the course, included in the overall courseload, will be taught by visiting professor Paul Jenkins (University of Warwick, UK) on Simulation and inference in population genetics (cf. International visiting professors).
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Results of learning outcomes
Knowledge and understanding: after the course, the student will have a sufficient knowledge of discrete time markov chains, continous time Markov chains, of the basic Monte Carlo sampling schemes and Markov chain Monte Carlo algorithms, and of Levy processes. The student will know the main properties of these objects and how to prove these formally.
Applying knowledge and understanding: the student will be able to manipulate the mathematical objects seen in the course in order to elicit and prove their properties, for example verifying its reversibility; finding its stationary distribution, etc. The student will also be able to construct a Markov chain Monte Carlo algorithm for a problem at hand (of comparable difficulty to those analysied in class) by choosing and appropriately adapting one of the strategies seen in class. The acquired knowledge will suffice for reading and understanding independently a research article on topics coherent with the course contents.
Making judgements: the student will be able to classify a Markov chain and a continuous time Markov chain in terms of irreducibility, periodicity, recurrence, stationarity and reversibility.
Communication skills: the student will be able to explain to a non expert the acquired concepts by using a formallly correct and rigourous exposition, and to discuss with experts about topics coherent with the course contents.
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Course delivery
The course is composed of 24 lectures for a total of 48 hours. Upon persistence ofer persistence of the Covid-19 emergency, the classes in a.y. 2020/21 will be held online.
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Learning assessment methods
The final assessment consists in a written examination on the material covered in class. The verification will evaluate the student's detailed knowledge and comprehension of the topics covered, together with the propriety of language, rigour and appropriate level of mathematical formality used for presenting definitions, examples, results and proofs.
Upon persistence of the Covid-19 emergency the final assessment will take place online with Webex video surveillance. Detailed instruction are published on the course's Moodle page (link below).
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Program
- Markov chains: transition matrices, Chapman-Kolmogorov equations, strong Markov property, classification of states and chains, invariant measures and stationarity, reversibility, convergence to equilibrium and convergence of ergodic averages.
- Elements of Monte Carlo and Markov chain Monte Carlo methods: Monte Carlo sampling; rejection sampling; importance sampling; Markov chain Monte Carlo principle; thinning; Metropolis-Hastings algorithm; Gibbs sampler; slice sampler; mixtures and cycles of MCMC.
- Continuous time Markov chains: transition functions, transition rates and infinitesimal generators; backward and forward Kolmogorov equations; embedded chains and holding times; uniform chains; stationarity; reversibility; scaling limits and diffusion approximations.
- Levy processes: definition; infinite divisibility; Levy-Khintchine formula; Levy-Ito decomposition; Poisson random measures; processes with infinite activity.
The material introduced will be throughly discussed and illustrated with numerous examples.
Suggested readings and bibliography
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Main references:
- NORRIS, J.R. Markov chains. Cambridge Series in Statistical and Probabilistic Mathematics.
- BREMAUD, P. Markov Chains. Springer.
Further suggested readings:
- BILLINGSLEY, P. Probability and measure. Wiley.
- GRIMMETT, G.R. and STIRZAKER, D.R. Probability and random processes. Oxford University Press.
- KARLIN and TAYLOR. A first Course in Stochastic Processes. Academic Press.
- KARLIN and TAYLOR. A second Course in Stochastic Processes. Academic Press.
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Class schedule
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