Table R - Higher Degree By Research

Unit outlines will be available through Find a unit outline.

Table R - Advanced Numerical Analyses

This table lists Table R - Higher Degree by Research units of study
BUSS7904 Advanced Quantitative Methods

Credit points: 6 Session: Semester 2 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Prohibitions: ECOF7904 Assumed knowledge: BUSS7902 Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Refer to the unit of study outline https://www.sydney.edu.au/units
This unit provides students with an introduction to advanced quantitative analysis techniques that they may be required to know, discuss or conduct, both during their PhD and in their future working lives. The unit is divided into four segments. The first segment reviews basic quantitative methods covered in BUSS7902 before considering issues around estimation and forecasting. Focus then switches to approaches for dealing with repeated measures including panel estimation methods and time series analysis, before consideration of ANOVA techniques and analogous non-parametric methods. Consideration is then given to the most widely used multivariate methods including factor analysis, multiple discriminant analysis, cluster analysis and structural equation modelling. The final segment covers categorical and discrete choice data analysis covering both the theory and practice of designing choice experiments and conducting sophisticated logit modelling applications. The unit covers both the theory and application of the various techniques with hands-on lab-based sessions and assignments crucial to the quality of the learning experience.
AMME5060 Advanced Computational Engineering

Credit points: 6 Session: Semester 2 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Prerequisites: UG students are required to complete AMME3060 before enrolling in this unit. Assumed knowledge: Linear algebra, calculus and partial differential equations, Taylor series, the finite difference and finite element methods, numerical stability, accuracy, direct and iterative linear solvers and be able to write Matlab Scripts to solve problems using these methods. Assessment: Refer to the assessment table in the unit outline. Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Department permission required for enrolment
This unit will cover advanced numerical and computational methods within an engineering context. The context will include parallel coding using MPI, computational architecture, advanced numerical methods including spectral methods, finite difference schemes and efficient linear solvers including multi-grid solvers and Krylov subspace solvers. Students will develop to skills and confidence to write their own computational software. Applications in fluid and solid mechanics will be covered.
DATA5441 Networks and High-dimensional Inference

Credit points: 6 Teacher/Coordinator: Refer to the unit of study outline https://www.sydney.edu.au/units Session: Semester 1 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Assumed knowledge: Linear algebra (matrices, eigenvalues, etc.); introductory concepts in statistics (statistical models, inference); a programming language. Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units Mode of delivery: Normal (lecture/lab/tutorial) day
In our interconnected world, networks are an increasingly important representation of datasets and systems. This unit will investigate how this network approach to problems can be pursued through the combination of mathematical models and datasets. You will learn different mathematical models of networks and understand how these models explain non-intuitive phenomena, such as the small world phenomenon (short paths between nodes despite clustering), the friendship paradox (our friends typically have more friends than we have), and the sudden appearance of epidemic-like processes spreading through networks. You will learn computational techniques needed to infer information about the mathematical models from data and, finally, you will learn how to combine mathematical models, computational techniques, and real-world data to draw conclusions about problems. More generally, network data is a paradigm for high-dimensional interdependent data, the typical problem in data science. By doing this unit you will develop computational and mathematical skills of wide applicability in studies of networks, data science, complex systems, and statistical physics.
DATA5710 Applied Statistics for Complex Data

Credit points: 6 Teacher/Coordinator: Refer to the unit of study outline https://www.sydney.edu.au/units Session: Intensive August,Intensive March Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Assumed knowledge: Familiarity with probability theory at 4000 level (e.g., STAT4211 or STAT4214 or equivalent) and with statistical modelling (e.g., STAT4027 or equivalent). Please consult with the coordinator for further information. Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units Mode of delivery: Block mode
Note: Department permission required for enrolment
Note: This unit is only available in even years.
With explosions in availability of computing power and facilities for gathering data in recent times, a key skill of any graduate is the ability to work with increasingly complex datasets. There may include, for example, data sets with multiple levels of observations gathered from diverse sources using a variety of methods. Being able to apply computational skills to implement appropriate software, as well as bringing to bear statistical expertise in the design of the accompanying algorithms are both vital when facing the challenge of analysing complicated data. This unit is made up of three distinct modules, each focusing on a different aspect of applications of statistical methods to complex data. These include (but are not restricted to) the development of a data product that interrogate large and complicated data structures; using sophisticated statistical methods to improve computational efficiency for large data sets or computationally intensive statistical methods; and the analysis of categorical ordinal data. Across all modules you will develop expertise in areas of statistical methodology, statistical analysis as well as computational statistics. Additional modules may be delivered, depending on the areas of expertise of available staff and distinguished visitors.
DATA5711 Bayesian Computational Statistics

Credit points: 6 Teacher/Coordinator: Refer to the unit of study outline https://www.sydney.edu.au/units Session: Intensive August,Intensive March Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Assumed knowledge: Familiarity with probability theory at 4000 level (e.g., STAT4211 or STAT4214 or equivalent) and with statistical modelling (e.g., STAT4027 or equivalent). Please consult with the coordinator for further information. Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units Mode of delivery: Block mode
Note: Department permission required for enrolment
Note: This unit is only available in odd years.
Increased computing power has meant that many Bayesian methods can now be easily implemented and provide solutions to problems that have previously been intractable. Bayesian methods allow researchers to incorporate prior knowledge into their statistical models. This unit is made up of three distinct modules, each focusing on a different niche in the application of Bayesian statistical methods to complex data in, for example, geophysics, ecology and hydrology. These include (but are not restricted to) Bayesian methods and models; statistical inversion; approximate Bayesian inference for semiparametric regression. Across all modules you will develop expertise in Bayesian computational statistics. On completion of this unit you will be able to apply appropriate Bayesian methods to a variety of applications in science, and other data-heavy disciplines to develop a better understanding of the information inherent in complex datasets.
OLET5602 Computational Analysis for omics Data

Credit points: 2 Teacher/Coordinator: Refer to the unit of study outline https://www.sydney.edu.au/units Session: Intensive August Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Assumed knowledge: Experience with at least one programming language. Basic computational and statistical concepts. Basic knowledge of molecular biology. Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units Mode of delivery: Block mode
Molecular and systems biology have become data-intensive sciences owing to the fast-growing omics technologies that enable the profiling of genome, epigenome, transcriptome, and proteome at full scale and, increasingly, at the single-cell level. Computational and statistical methodologies are now indispensable for analysing omics data generated from high-throughput technologies. This unit will introduce you to commonly used computational and statistical methods in omics data analysis. You are encouraged to use your own data to construct the models to visualise your research and interpret results. Learning the correct use of computational methods for various omics data analysis applications including your own data, you will develop an essential knowledge of methods and techniques in analysing omics data. This will provide a strong foundation for using computational approaches in omics-based molecular and systems biology research.
Textbooks
A First Course in Systems Biology, Eberhard O. Voit, (Garland Science, 2017).
STAT5003 Computational Statistical Methods

Credit points: 6 Teacher/Coordinator: Refer to the unit of study outline https://www.sydney.edu.au/units Session: Semester 1,Semester 2 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Assumed knowledge: STAT5002 or equivalent introductory statistics course with a statistical computing component Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units Mode of delivery: Normal (lecture/lab/tutorial) evening
Note: Department permission required for enrolment
The objectives of this unit of study are to develop an understanding of modern computationally intensive methods for statistical learning, inference, exploratory data analysis and data mining. Advanced computational methods for statistical learning will be introduced, including clustering, density estimation, smoothing, predictive models, model selection, combinatorial optimisation methods, sampling methods, the Bootstrap and Monte Carlo approach. In addition, the unit will demonstrate how to apply the above techniques effectively for use on large data sets in practice.
Textbooks
An Introduction to Statistical Learning (with Applications in R), Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, 2014, Springer.
STAT5610 Advanced Inference

This unit of study is not available in 2021

Credit points: 6 Teacher/Coordinator: Oded Yacobi Session: Semester 2 Classes: 3 x 1hr lecture/week, 1 x 1hr tutorial/week Assumed knowledge: Familiarity with probability theory at 4000 level (e.g., STAT4211 or STAT4214 or equivalent) and with statistical modelling (e.g., STAT4027 or equivalent). Please consult with the coordinator for further information. Assessment: assignments x 4 (27%), submitted homework x 3 (15%), quizzes x 3 (58%) Mode of delivery: Normal (lecture/lab/tutorial) day
Note: This unit is only available in even years.
The great power of the discipline of Statistics is the possibility to make inferences concerning a large population based on optimally learning from increasingly large and complex data. Critical to successful inference is a deep understanding of the theory when the number of samples and the number of observed features is large and require complex statistical methods to be analysed correctly. In this unit you will learn how to integrate concepts from a diverse suite of specialities in mathematics and statistics such as optimisation, functional approximations and complex analysis to make inferences for highly complicated data. In particular, this unit explores advanced topics in statistical methodology examining both theoretical foundations and details of implementation to applications. The unit is made up of 3 distinct modules. These include (but are not restricted to) Asymptotic theory for statistics and econometrics, Theory and algorithms for statistical learning with big data, and Introduction to optimal semiparametric optimality.
STAT5611 Statistical Methodology

Credit points: 6 Teacher/Coordinator: Refer to the unit of study outline https://www.sydney.edu.au/units Session: Semester 2 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Assumed knowledge: Familiarity with probability theory at 4000 level (e.g., STAT4211 or STAT4214 or equivalent) and with statistical modelling (e.g., STAT4027 or equivalent). Please consult with the coordinator for further information. Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units Mode of delivery: Normal (lecture/lab/tutorial) day
Note: This unit is only available in odd years.
The great power of the discipline of Statistics is the possibility to make inferences concerning a large population based on only observing a relatively small sample from it. Of course, this "magic" does not come without a price, we must construct statistical models to approximate these populations and samples from them, develop mathematical tools using probability theory, appreciate the limitations of our methods and, most importantly, understand what assumptions need to be made for such inferences to be valid, and develop ways to check these assumptions. Implementing these methods to possibly complex data structures is also a challenge that must be overcome. This unit explores advanced topics in statistical methodology examining both theoretical foundations and details of implementation to applications. The unit is made up of 3 distinct modules. These include (but are not restricted to) Advanced Survival Analysis, Extreme Value Theory and Statistical Methods in Bioinformatics.