Course Description
This is a compulsory course for the first year Masters' (Statistics) students. Familiarity with Real Analysis will be assumed. A prior course on basic Probability distributions is a prerequisite (MSO201A or MTH431A or equivalent). We shall discuss the Mathematical foundations of Probability in this course.
Course Content
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$\sigma$-field of events (emphasis on $\sigma$-fields generated by a collection of sets, Borel $\sigma$-fields), Limits of sequences of sets. Principle of good sets (Monotone Class Theorem, Dynkin's $\pi-\lambda$ Theorem).
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Measure and Measure space (emphasis on probability measure and probability space), properties, Borel-Cantelli Lemma (first half).
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Extension of measures (existence and uniqueness), Outer Measure, Carath\'eodery Extension Theorem for $\sigma$-finite measure. Completeness of measure spaces.
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Measurable functions (emphasis on Random variables), algebraic properties and limits of measurable functions. Probability distribution of Random variables, induced probability space, distribution function.
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Correspondence between distribution functions and Lebesgue-Stieltjes measures (emphasis on probability measures), construction of Lebesgue measure.
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Integration of measurable functions (emphasis on Expectation and moments of Random variables, change of variables/measures), inequalities.
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Convergence theorems for expectations of sequences of random variables (monotone convergence theorem, Fatou's lemma, dominated convergence theorem).
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Connection between Riemann and Lebesgue Integration.
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Various modes of convergence of sequences of random variables (almost surely, in probability, in $r$-th mean). $L^p$ spaces.
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Independence of events and random variables. Borel-Cantelli Lemma (second half).
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Convergence of series of independent random variables, Kolmogorov inequality, Kolmogorov three-series
criterion. -
Characteristic function and its properties, inversion formulae.
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Weak convergence of probability measures, connection with convergence of sequences of distribution functions, Helly-Bray theorems, Portmanteu Theorem.
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Khintchin's weak law of large numbers, Kolmogorov strong law of large numbers. Central limit
theorems of Lindeberg-Levy, Liapounov and Lindeberg-Feller.
Course Audience
This is a compulsory course for M.Sc (Statistics) students. Students pursuing other degrees may take it as a Departmental/Open Elective.