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Showing 25 course outlines from 2938 matches

2251

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
2252

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
2253

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
2254

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
2255

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
2256

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
2257

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
2258

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
2259

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
2260

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
2261

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
2262

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
2263

STATS 731

: Bayesian Inference
2023 Semester Two (1235)
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
2264

STATS 731

: Bayesian Inference
2022 Semester Two (1225)
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 210 or 225
2265

STATS 731

: Bayesian Inference
2021 Semester Two (1215)
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 210 or 225
2266

STATS 731

: Bayesian Inference
2020 Semester One (1203)
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 210 or 225
2267

STATS 732

: Foundations of Statistical Inference
2023 Semester One (1233)
Fundamentals of statistical inference including estimation, hypothesis testing, likelihood methods, multivariate distributions, joint, marginal, and conditional distributions, vector random variables, and an introduction to decision theory and Bayesian inference.
Subject: Statistics
Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250
Restriction: STATS 310
2268

STATS 732

: Foundations of Statistical Inference
2022 Semester One (1223)
Fundamentals of statistical inference including estimation, hypothesis testing, likelihood methods, multivariate distributions, joint, marginal, and conditional distributions, vector random variables, and an introduction to decision theory and Bayesian inference.
Subject: Statistics
Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250
Restriction: STATS 310
2269

STATS 732

: Foundations of Statistical Inference
2021 Semester One (1213)
Fundamentals of statistical inference including estimation, hypothesis testing, likelihood methods, multivariate distributions, joint, marginal, and conditional distributions, vector random variables, and an introduction to decision theory and Bayesian inference.
Subject: Statistics
Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250
Restriction: STATS 310
2270

STATS 732

: Foundations of Statistical Inference
2020 Semester One (1203)
Fundamentals of statistical inference including estimation, hypothesis testing, likelihood methods, multivariate distributions, joint, marginal, and conditional distributions, vector random variables, and an introduction to decision theory and Bayesian inference.
Subject: Statistics
Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250
Restriction: STATS 310
2271

STATS 740

: Sample Surveys
2022 Semester Two (1225)
The design, management and analysis of sample surveys. Topics such as the following are studied. Types of Survey. Revision of statistical aspects of sampling. Preparing surveys. Research entry: problem selection, sponsorship and collaboration. Research design: methodology and data collection; Issues of sample design and sample selection. Conducting surveys: Questionnaires and questions; Non-sampling issues; Project management; Maintaining data quality. Concluding surveys: Analysis; Dissemination.
Subject: Statistics
Prerequisite: 15 points from STATS 240, 330, 340, and 15 points from Stage II Mathematics
2272

STATS 740

: Sample Surveys
2021 Semester Two (1215)
The design, management and analysis of sample surveys. Topics such as the following are studied. Types of Survey. Revision of statistical aspects of sampling. Preparing surveys. Research entry: problem selection, sponsorship and collaboration. Research design: methodology and data collection; Issues of sample design and sample selection. Conducting surveys: Questionnaires and questions; Non-sampling issues; Project management; Maintaining data quality. Concluding surveys: Analysis; Dissemination.
Subject: Statistics
Prerequisite: 15 points from STATS 240, 330, 340, and 15 points from Stage II Mathematics
2273

STATS 740

: Sample Surveys
2020 Semester Two (1205)
The design, management and analysis of sample surveys. Topics such as the following are studied. Types of Survey. Revision of statistical aspects of sampling. Preparing surveys. Research entry: problem selection, sponsorship and collaboration. Research design: methodology and data collection; Issues of sample design and sample selection. Conducting surveys: Questionnaires and questions; Non-sampling issues; Project management; Maintaining data quality. Concluding surveys: Analysis; Dissemination.
Subject: Statistics
Prerequisite: 15 points from STATS 340, 741 and 15 points from STATS 310, 732
2274

STATS 747

: Statistical Methods in Marketing
2021 Semester Two (1215)
Stochastic models of brand choice, applications of General Linear Models in marketing, conjoint analysis, advertising media models and marketing response models.
Subject: Statistics
Prerequisite: 15 points from BIOSCI 209, STATS 201, 207, 208, 210, 707
2275

STATS 747

: Statistical Methods in Marketing
2020 Semester Two (1205)
Stochastic models of brand choice, applications of General Linear Models in marketing, conjoint analysis, advertising media models and marketing response models.
Subject: Statistics
No pre-requisites or restrictions