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MATH3871 / MATH5960 Bayesian Inference and Computation
创建日期：2024-04-16 16:03:39
所属分类：数学mathematics
标签： MATH3871
Bayesian Inference and Computation

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MATH3871 / MATH5960

Bayesian Inference and Computation

Course Aims

? Provide a strong background in the concepts and philosophy of Bayesian inference.
? Instil an appreciation of the benefits of the Bayesian framework.
? Provide extensive practical opportunities to implement Bayesian data analyses.
? Present an overview of research activity in this field.
Course Description

After describing the fundamentals of Bayesian inference, this course will examine the specification
of prior and posterior distributions, Bayesian decision theoretic concepts, the ideas behind
Bayesian hypothesis tests, model choice and model averaging, and evaluate the capabilities of
several common model types, such as hierarchical and mixture models. An important part of
Bayesian inference is the requirement to numerically evaluate complex integrals on a routine
basis. Accordingly, this course will also introduce the ideas behind Monte Carlo integration,
importance sampling, rejection sampling, Markov chain Monte Carlo samplers such as the Gibbs
sampler and the Metropolis-Hastings algorithm and use of the WinBuGS posterior simulation
software.

Late Submission of Assessment Tasks
A late penalty of 5% of the awarded mark will be applied per day or part day any assessment task
is submitted more than 1 hour late. (Where "late" in this context means after any extensions
granted for Special Consideration or Equitable Learning Provisions.) For example, an assessment
task that was awarded 75% would be given 65% if it was 1-2 days late. Any assessment task
submitted 7 or more days late will be given zero.

Note that the penalty does not apply to
? Assessment tasks worth less than 5% of the total course mark, e.g. weekly quizzes,
weekly class participation, or weekly homework tasks.
? Examinations and examination-style class tests
? Pass/Fail Assessments

Course Schedule

The course will include material taken from some of the following topics. This is should only serve
as a guide as it is not an extensive list of the material to be covered and the timings are
approximate. The course content is ultimately defined by the material covered in lectures.

Weeks Topic Reading (if
applicable)
1 Introduction to subjective probability and differences
between Bayesian and classical statistics
Refer to Moodle

2 Prior and posterior distributions Refer to Moodle

3 Point estimation, interval estimation and predictive
distributions
Refer to Moodle

4 Bayesian analysis of normal models Refer to Moodle

5 Introduction to Monte Carlo methods Refer to Moodle

7 MCMC methods Refer to Moodle

8 Bayesian hypothesis testing Refer to Moodle

9 Linear and generalised linear models Refer to Moodle

10 Hierarchical models Refer to Moodle


Textbooks

Course Learning Outcomes (CLO)

? Provide a background in the concepts and philosophy of Bayesian inference.
? Demonstrate an understanding of how common model type’s work and be able to construct
models for new problems.

? Show an appreciation of the importance of computational techniques in Bayesian
inference.
? Perform real-world Bayesian data analyses.

Moodle

Log in to Moodle to find announcements, general information, notes, lecture slide, classroom tutorial
and assessments etc.

The School of Mathematics and Statistics has adopted a number of policies relating to enrolment,
attendance, assessment, plagiarism, cheating, special consideration etc. These are in addition to
the Policies of The University of New South Wales. Individual courses may also adopt other
policies in addition to or replacing some of the School ones. These will be clearly notified in the
Course Initial Handout and on the Course Home Pages on the Maths Stats web site.

Students in courses run by the School of Mathematics and Statistics should be aware of the School
and Course policies by reading the appropriate pages on the Maths Stats web site starting at

The School of Mathematics and Statistics will assume that all its students have read and
understood the School policies on the above pages and any individual course policies on the
Course Initial Handout and Course Home Page. Lack of knowledge about a policy will not be an
excuse for failing to follow the procedure in it.

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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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MATH20602 COMP532 MAS275 COMP5318 COMP4002 FIT5086 MAST20004 FIT3152 FIT5129 CIS2380 CS521 COMP 1005 EECMS CITS5501 ECE 462 ECON 6002 ENG 4051 CSC 648 CMT654 MECHENG 713 PHYS1002 AMME 2000 FP0060 DDES3200 MECHENG 743 BINF90002 FIT2081 ISYS90081 ELECTENG 722 BUS 360 ECOS3010 BUS2810 ETF2100 ENG2048 STAT5002 COMP2017 CEME 2003 MATS3004 COMP9319 PHYS1001 COMP5349 AMME2261 COMP-381 FIT3173 COSC2757 ENG4025 QBUS2820 COMP9414 MATH2021 EOSC118 MFIN6690 PMATH 321 CSE-141L MATH575A COMPSCI5002 COMP1511 management comp2123 FINS3616 COMP 3331 COMS30034 DTSC 71 AcF 702 AcF702 DTSC71 MAT021 ELE8083 ACFI213 EG-127J 127J FIT5163 FIT2001 ELE00113M CMT310 CS 245 ENGL 1101 MTH6115 EE497 CS 325 Math 124A MTH5130 MBA502 MGT 5380 ARCH 1261 F033800 20LLP109 ELE4009 FINANCIAL COMP3331 Computer ECE4179 20T3 COMP9311 COMP9311 UNSW MFIN6210 ACCT 5930 EE590 ENG2029 ECO100 SIT315 ACCT3610 COMP3600 STK2100 FIT2004 COMP30020 ACTL1101 Stat 101B BANA 200 COSC2473 ENGG1400 AI COMP90043 MMF 1914H PHYS 127 CS584 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