Search Course Outline
Showing 25 course outlines from 3701 matches
2851
STATS 731
: Bayesian Inference2024 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.
Prerequisite: STATS 331 and 15 points from STATS 210, 225
2852
STATS 731
: Bayesian Inference2023 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.
Prerequisite: STATS 331 and 15 points from STATS 210, 225
2853
STATS 731
: Bayesian Inference2022 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.
Prerequisite: STATS 210 or 225
2854
STATS 731
: Bayesian Inference2021 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.
Prerequisite: STATS 210 or 225
2855
STATS 731
: Bayesian Inference2020 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.
Prerequisite: STATS 210 or 225
2856
STATS 732
: Foundations of Statistical Inference2024 Semester One (1243)
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.
Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250
Restriction: STATS 310
Restriction: STATS 310
2857
STATS 732
: Foundations of Statistical Inference2023 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.
Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250
Restriction: STATS 310
Restriction: STATS 310
2858
STATS 732
: Foundations of Statistical Inference2022 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.
Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250
Restriction: STATS 310
Restriction: STATS 310
2859
STATS 732
: Foundations of Statistical Inference2021 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.
Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250
Restriction: STATS 310
Restriction: STATS 310
2860
STATS 732
: Foundations of Statistical Inference2020 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.
Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250
Restriction: STATS 310
Restriction: STATS 310
2861
STATS 740
: Sample Surveys2024 Semester One (1243)
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.
Prerequisite: 15 points from STATS 240, 330, 340, and 15 points from Stage II Mathematics
2862
STATS 740
: Sample Surveys2022 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.
Prerequisite: 15 points from STATS 240, 330, 340, and 15 points from Stage II Mathematics
2863
STATS 740
: Sample Surveys2021 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.
Prerequisite: 15 points from STATS 240, 330, 340, and 15 points from Stage II Mathematics
2864
STATS 740
: Sample Surveys2020 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.
Prerequisite: 15 points from STATS 340, 741 and 15 points from STATS 310, 732
2865
STATS 747
: Statistical Methods in Marketing2021 Semester Two (1215)
Stochastic models of brand choice, applications of General Linear Models in marketing, conjoint analysis, advertising media models and marketing response models.
Prerequisite: 15 points from BIOSCI 209, STATS 201, 207, 208, 210, 707
2866
STATS 747
: Statistical Methods in Marketing2020 Semester Two (1205)
Stochastic models of brand choice, applications of General Linear Models in marketing, conjoint analysis, advertising media models and marketing response models.
No pre-requisites or restrictions
2867
STATS 760
: A Survey of Modern Applied Statistics2020 Semester One (1203)
A survey of techniques from modern applied statistics. Topics covered will be linear, non-linear and generalised linear models, modern regression including CART and neural networks, mixed models, survival analysis, time series and spatial statistics.
Prerequisite: STATS 310, 330
2868
STATS 762
: Regression for Data Science2024 Semester One (1243)
Application of the generalised linear model to fit data arising from a wide range of sources, including multiple linear regression models, Poisson regression, and logistic regression models. The graphical exploration of data. Model building for prediction and for causal inference. Other regression models such as quantile regression. A basic understanding of vector spaces, matrix algebra and calculus will be assumed.
Prerequisite: 15 points from STATS 210, 225, 707, and 15 points from ENGSCI 314, STATS 201, 207, 208
Restriction: STATS 330
Restriction: STATS 330
2869
STATS 762
: Regression for Data Science2023 Semester One (1233)
Application of the generalised linear model to fit data arising from a wide range of sources, including multiple linear regression models, Poisson regression, and logistic regression models. The graphical exploration of data. Model building for prediction and for causal inference. Other regression models such as quantile regression. A basic understanding of vector spaces, matrix algebra and calculus will be assumed.
Prerequisite: STATS 707 or 210 or 225, and 15 points from STATS 201, 207, 208 or a B+ or higher in BIOSCI 209
Restriction: STATS 330
Restriction: STATS 330
2870
STATS 762
: Regression for Data Science2022 Semester One (1223)
Application of the generalised linear model to fit data arising from a wide range of sources, including multiple linear regression models, Poisson regression, and logistic regression models. The graphical exploration of data. Model building for prediction and for causal inference. Other regression models such as quantile regression. A basic understanding of vector spaces, matrix algebra and calculus will be assumed.
Prerequisite: STATS 707 or 210 or 225, and 15 points from STATS 201, 207, 208 or a B+ or higher in BIOSCI 209
Restriction: STATS 330
Restriction: STATS 330
2871
STATS 762
: Regression for Data Science2021 Semester One (1213)
Application of the generalised linear model to fit data arising from a wide range of sources, including multiple linear regression models, Poisson regression, and logistic regression models. The graphical exploration of data. Model building for prediction and for causal inference. Other regression models such as quantile regression. A basic understanding of vector spaces, matrix algebra and calculus will be assumed.
Prerequisite: STATS 707 or 210 or 225, and 15 points from STATS 201, 207, 208 or a B+ or higher in BIOSCI 209
Restriction: STATS 330
Restriction: STATS 330
2872
STATS 762
: Regression for Data Science2020 Semester One (1203)
Application of the generalised linear model to fit data arising from a wide range of sources, including multiple linear regression models, Poisson regression, and logistic regression models. The graphical exploration of data. Model building for prediction and for causal inference. Other regression models such as quantile regression. A basic understanding of vector spaces, matrix algebra and calculus will be assumed.
Prerequisite: STATS 707 or 210 or 225, and 15 points from STATS 201, 207, 208 or a B+ or higher in BIOSCI 209
Restriction: STATS 330
Restriction: STATS 330
2873
STATS 763
: Advanced Regression Methodology2024 Semester Two (1245)
Generalised linear models, generalised additive models, survival analysis. Smoothing and semiparametric regression. Marginal and conditional models for correlated data. Model selection for prediction and for control of confounding. Model criticism and testing. Computational methods for model fitting, including Bayesian approaches.
Prerequisite: STATS 210 or 225, and 15 points from STATS 330, 762 and 15 points at Stage II in Mathematics
2874
STATS 763
: Advanced Regression Methodology2023 Semester Two (1235)
Generalised linear models, generalised additive models, survival analysis. Smoothing and semiparametric regression. Marginal and conditional models for correlated data. Model selection for prediction and for control of confounding. Model criticism and testing. Computational methods for model fitting, including Bayesian approaches.
Prerequisite: STATS 210 or 225, and 15 points from STATS 330, 762 and 15 points at Stage II in Mathematics
2875
STATS 763
: Advanced Regression Methodology2022 Semester One (1223)
Generalised linear models, generalised additive models, survival analysis. Smoothing and semiparametric regression. Marginal and conditional models for correlated data. Model selection for prediction and for control of confounding. Model criticism and testing. Computational methods for model fitting, including Bayesian approaches.
Prerequisite: STATS 210 or 225, and 15 points from STATS 330, 762 and 15 points at Stage II in Mathematics
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