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Showing 25 course outlines from 3701 matches
2826
STATS 720
: Stochastic Processes2023 Semester One (1233)
Continuous-time jump Markov processes. A selection of topics from: point processes, renewal theory, martingales, Brownian motion, Gaussian processes and inference for stochastic processes.
Prerequisite: STATS 320 or 325
2827
STATS 720
: Stochastic Processes2022 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.
Prerequisite: STATS 320 or 325
2828
STATS 720
: Stochastic Processes2021 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.
Prerequisite: STATS 320 or 325
2829
STATS 720
: Stochastic Processes2020 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.
Prerequisite: STATS 320 or 325
2830
STATS 721
: Foundations of Stochastic Processes2024 Semester Two (1245)
Fundamentals of stochastic processes. Topics include: generating functions, branching processes, Markov chains, and random walks.
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
Restriction: STATS 325
2831
STATS 721
: Foundations of Stochastic Processes2023 Semester Two (1235)
Fundamentals of stochastic processes. Topics include: generating functions, branching processes, Markov chains, and random walks.
Restriction: STATS 325
2832
STATS 721
: Foundations of Stochastic Processes2022 Semester Two (1225)
Fundamentals of stochastic processes. Topics include: generating functions, branching processes, Markov chains, and random walks.
Restriction: STATS 325
2833
STATS 721
: Foundations of Stochastic Processes2021 Semester Two (1215)
Fundamentals of stochastic processes. Topics include: generating functions, branching processes, Markov chains, and random walks.
Restriction: STATS 325
2834
STATS 721
: Foundations of Stochastic Processes2020 Semester Two (1205)
Fundamentals of stochastic processes. Topics include: generating functions, branching processes, Markov chains, and random walks.
Restriction: STATS 325
2835
STATS 722
: Foundations of Financial Mathematics2020 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.
Prerequisite: 15 points at Stage II in Statistics or BIOSCI 209, and 15 points at Stage II in Mathematics
Restriction: STATS 370
Restriction: STATS 370
2836
STATS 723
: Stochastic Methods in Finance2022 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.
Prerequisite: STATS 125 and 370, or 15 points from STATS 210, 225, 325
2837
STATS 723
: Stochastic Methods in Finance2021 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.
Prerequisite: STATS 125 and 370, or 15 points from STATS 210, 225, 325
2838
STATS 723
: Stochastic Methods in Finance2020 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.
Prerequisite: STATS 125 and 370, or 15 points from STATS 210, 225, 325
2839
STATS 726
: Time Series2024 Semester Two (1245)
Stationary processes, modelling and estimation in the time domain, forecasting and spectral analysis.
Prerequisite: STATS 210, and 15 points from STATS 326, 786
2840
STATS 726
: Time Series2023 Semester Two (1235)
Stationary processes, modelling and estimation in the time domain, forecasting and spectral analysis.
Prerequisite: STATS 210, and 320 or 325
2841
STATS 726
: Time Series2022 Semester Two (1225)
Stationary processes, modelling and estimation in the time domain, forecasting and spectral analysis.
Prerequisite: STATS 210, and 320 or 325
2842
STATS 726
: Time Series2021 Semester Two (1215)
Stationary processes, modelling and estimation in the time domain, forecasting and spectral analysis.
Prerequisite: STATS 210, and 320 or 325
2843
STATS 726
: Time Series2020 Semester Two (1205)
Stationary processes, modelling and estimation in the time domain, forecasting and spectral analysis.
No pre-requisites or restrictions
2844
STATS 727
: Foundations of Applied Time Series Analysis2021 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.
Prerequisite: 15 points from BIOSCI 209, ECON 221, STATS 201, 207, 208, 707
Restriction: STATS 326
Restriction: STATS 326
2845
STATS 727
: Foundations of Applied Time Series Analysis2020 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.
Prerequisite: 15 points from BIOSCI 209, ECON 221, STATS 201, 207, 208
Restriction: STATS 326
Restriction: STATS 326
2846
STATS 730
: Statistical Inference2024 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.
Prerequisite: STATS 310 or 732
2847
STATS 730
: Statistical Inference2023 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.
Prerequisite: STATS 310 or 732
2848
STATS 730
: Statistical Inference2022 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.
Prerequisite: STATS 310 or 732
2849
STATS 730
: Statistical Inference2021 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.
Prerequisite: STATS 310 or 732
2850
STATS 730
: Statistical Inference2020 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.
Prerequisite: STATS 310 or 732
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