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Showing 25 course outlines from 3697 matches

2826

STATS 720

: Stochastic Processes
2022 Semester One (1223)
Continuous-time jump Markov processes. A selection of topics from: point processes, renewal theory, martingales, Brownian motion, Gaussian processes and inference for stochastic processes.
Subject: Statistics
Prerequisite: STATS 320 or 325
2827

STATS 720

: Stochastic Processes
2021 Semester One (1213)
Continuous-time jump Markov processes. A selection of topics from: point processes, renewal theory, martingales, Brownian motion, Gaussian processes and inference for stochastic processes.
Subject: Statistics
Prerequisite: STATS 320 or 325
2828

STATS 720

: Stochastic Processes
2020 Semester One (1203)
Continuous-time jump Markov processes. A selection of topics from: point processes, renewal theory, martingales, Brownian motion, Gaussian processes and inference for stochastic processes.
Subject: Statistics
Prerequisite: STATS 320 or 325
2829

STATS 721

: Foundations of Stochastic Processes
2024 Semester Two (1245)
Fundamentals of stochastic processes. Topics include: generating functions, branching processes, Markov chains, and random walks.
Subject: Statistics
Prerequisite: 15 points from STATS 125, 210, 225, 320 with at least a B+ and 15 points from MATHS 208, 250, 253
Restriction: STATS 325
2830

STATS 721

: Foundations of Stochastic Processes
2023 Semester Two (1235)
Fundamentals of stochastic processes. Topics include: generating functions, branching processes, Markov chains, and random walks.
Subject: Statistics
Restriction: STATS 325
2831

STATS 721

: Foundations of Stochastic Processes
2022 Semester Two (1225)
Fundamentals of stochastic processes. Topics include: generating functions, branching processes, Markov chains, and random walks.
Subject: Statistics
Restriction: STATS 325
2832

STATS 721

: Foundations of Stochastic Processes
2021 Semester Two (1215)
Fundamentals of stochastic processes. Topics include: generating functions, branching processes, Markov chains, and random walks.
Subject: Statistics
Restriction: STATS 325
2833

STATS 721

: Foundations of Stochastic Processes
2020 Semester Two (1205)
Fundamentals of stochastic processes. Topics include: generating functions, branching processes, Markov chains, and random walks.
Subject: Statistics
Restriction: STATS 325
2834

STATS 722

: Foundations of Financial Mathematics
2020 Semester Two (1205)
Fundamentals of financial mathematics. Topics include: mean-variance portfolio theory; options, arbitrage and put-call relationships; introduction of binomial and Black-Scholes option pricing models; compound interest, annuities, capital redemption policies, valuation of securities, sinking funds; varying rates of interest, taxation; duration and immunisation; introduction to life annuities and life insurance mathematics.
Subject: Statistics
Prerequisite: 15 points at Stage II in Statistics or BIOSCI 209, and 15 points at Stage II in Mathematics
Restriction: STATS 370
2835

STATS 723

: Stochastic Methods in Finance
2022 Semester One (1223)
Contingent claims theory in discrete and continuous time. Risk-neutral option pricing, Cox-Ross-Rubinstein and Black-Scholes models, stochastic calculus, hedging and risk management.
Subject: Statistics
Prerequisite: STATS 125 and 370, or 15 points from STATS 210, 225, 325
2836

STATS 723

: Stochastic Methods in Finance
2021 Semester One (1213)
Contingent claims theory in discrete and continuous time. Risk-neutral option pricing, Cox-Ross-Rubinstein and Black-Scholes models, stochastic calculus, hedging and risk management.
Subject: Statistics
Prerequisite: STATS 125 and 370, or 15 points from STATS 210, 225, 325
2837

STATS 723

: Stochastic Methods in Finance
2020 Semester One (1203)
Contingent claims theory in discrete and continuous time. Risk-neutral option pricing, Cox-Ross-Rubinstein and Black-Scholes models, stochastic calculus, hedging and risk management.
Subject: Statistics
Prerequisite: STATS 125 and 370, or 15 points from STATS 210, 225, 325
2838

STATS 726

: Time Series
2024 Semester Two (1245)
Stationary processes, modelling and estimation in the time domain, forecasting and spectral analysis.
Subject: Statistics
Prerequisite: STATS 210, and 15 points from STATS 326, 786
2839

STATS 726

: Time Series
2023 Semester Two (1235)
Stationary processes, modelling and estimation in the time domain, forecasting and spectral analysis.
Subject: Statistics
Prerequisite: STATS 210, and 320 or 325
2840

STATS 726

: Time Series
2022 Semester Two (1225)
Stationary processes, modelling and estimation in the time domain, forecasting and spectral analysis.
Subject: Statistics
Prerequisite: STATS 210, and 320 or 325
2841

STATS 726

: Time Series
2021 Semester Two (1215)
Stationary processes, modelling and estimation in the time domain, forecasting and spectral analysis.
Subject: Statistics
Prerequisite: STATS 210, and 320 or 325
2842

STATS 726

: Time Series
2020 Semester Two (1205)
Stationary processes, modelling and estimation in the time domain, forecasting and spectral analysis.
Subject: Statistics
No pre-requisites or restrictions
2843

STATS 727

: Foundations of Applied Time Series Analysis
2021 Semester One (1213)
Fundamentals of applied time series analysis. Topics include: components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas are presented.
Subject: Statistics
Prerequisite: 15 points from BIOSCI 209, ECON 221, STATS 201, 207, 208, 707
Restriction: STATS 326
2844

STATS 727

: Foundations of Applied Time Series Analysis
2020 Semester One (1203)
Fundamentals of applied time series analysis. Topics include: components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas are presented.
Subject: Statistics
Prerequisite: 15 points from BIOSCI 209, ECON 221, STATS 201, 207, 208
Restriction: STATS 326
2845

STATS 730

: Statistical Inference
2024 Semester Two (1245)
Fundamentals of likelihood-based inference, including sufficiency, conditioning, likelihood principle, statistical paradoxes. Theory and practice of maximum likelihood. Examples covered may include survival analysis, GLM's, nonlinear models, random effects and empirical Bayes models, and quasi-likelihood.
Subject: Statistics
Prerequisite: STATS 310 or 732
2846

STATS 730

: Statistical Inference
2023 Semester Two (1235)
Fundamentals of likelihood-based inference, including sufficiency, conditioning, likelihood principle, statistical paradoxes. Theory and practice of maximum likelihood. Examples covered may include survival analysis, GLM's, nonlinear models, random effects and empirical Bayes models, and quasi-likelihood.
Subject: Statistics
Prerequisite: STATS 310 or 732
2847

STATS 730

: Statistical Inference
2022 Semester Two (1225)
Fundamentals of likelihood-based inference, including sufficiency, conditioning, likelihood principle, statistical paradoxes. Theory and practice of maximum likelihood. Examples covered may include survival analysis, GLM's, nonlinear models, random effects and empirical Bayes models, and quasi-likelihood.
Subject: Statistics
Prerequisite: STATS 310 or 732
2848

STATS 730

: Statistical Inference
2021 Semester Two (1215)
Fundamentals of likelihood-based inference, including sufficiency, conditioning, likelihood principle, statistical paradoxes. Theory and practice of maximum likelihood. Examples covered may include survival analysis, GLM's, nonlinear models, random effects and empirical Bayes models, and quasi-likelihood.
Subject: Statistics
Prerequisite: STATS 310 or 732
2849

STATS 730

: Statistical Inference
2020 Semester Two (1205)
Fundamentals of likelihood-based inference, including sufficiency, conditioning, likelihood principle, statistical paradoxes. Theory and practice of maximum likelihood. Examples covered may include survival analysis, GLM's, nonlinear models, random effects and empirical Bayes models, and quasi-likelihood.
Subject: Statistics
Prerequisite: STATS 310 or 732
2850

STATS 731

: Bayesian Inference
2024 Semester Two (1245)
A course in practical Bayesian statistical inference covering: the Bayesian approach specification of prior distributions, decision-theoretic foundations, the likelihood principle, asymptotic approximations, simulation methods, Markov Chain Monte Carlo methods, the BUGS and CODA software, model assessment, hierarchical models, application in data analysis.
Subject: Statistics
Prerequisite: STATS 331 and 15 points from STATS 210, 225