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- Trans-dimensional Markov chain Monte Carlo Peter J. Green
- Delayed Rejection in Reversible Jump MetropolisHastings
- MATH11400 Statistics 1 201011 Homepage http://www.stats.bris.ac.uk/%7Emapjg/Teach/Stats1/
- Shrinkage and hierarchical models Suppose in each unit we observe a response assumed to have a
- Sampling decomposable graphs using a Markov chain on junction trees
- BCCS 2008/09: Graphical models and complex stochastic systems: Lecture 2: Statistical inference
- MATH11400 Statistics 1 201011 Homepage http://www.stats.bris.ac.uk/%7Emapjg/Teach/Stats1/
- BCCS 2008/09: Graphical models and complex stochastic systems: Exercises 5
- R: A self-learn tutorial 1 Introduction
- MATH11400 Statistics 1 201011 Homepage http://www.stats.bris.ac.uk/%7Emapjg/Teach/Stats1/
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- MATH11400 Statistics 1 201011 Homepage http://www.stats.bris.ac.uk/%7Emapjg/Teach/Stats1/
- Using R for Statistics practical work: Statistics 2 edition Introduction
- Bayesian growth curves using normal mixtures with nonparametric weights
- MATH11400 Statistics 1 201011 Homepage http://www.stats.bris.ac.uk/%7Emapjg/Teach/Stats1/
- Linear Models sheet 5 1. Given a `two-way' table {Yij, i = 1, 2, . . . , r; j = 1, 2, . . . , c}, let Yi. = c-1
- Grappa: R functions for probability propagation Peter J. Green
- Model Choice using Reversible Jump Markov Chain Monte Carlo,
- Mixture models in measurement error problems, with reference to
- Bayesian Modelling B sheet 1 1. Two rational mathematics students, A and B, play the following game. An unknown amount
- MATH11400 Statistics 1 201011 Homepage http://www.stats.bris.ac.uk/%7Emapjg/Teach/Stats1/
- Fully Bayesian reconstructions from single photon emission computed
- If you knew which coin had been selected, this probability would be either 0.8 (coin A) or 0.2 (coin B) and would again be
- Monte Carlo Integration Suppose we can draw samples from a distribution for , i.e.
- MATH11400 Statistics 1 201011 Homepage http://www.stats.bris.ac.uk/%7Emapjg/Teach/Stats1/
- BCCS 2008/09: Graphical models and complex stochastic systems: Exercises 1
- 84 IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 9. NO. I. MARCH 1990 Bayesian Reconstructions From Emission
- MATH11400 Statistics 1 201011 Homepage http://www.stats.bris.ac.uk/%7Emapjg/Teach/Stats1/
- Linear Models sheet 2 1. For each of the following simple linear models, write down the appropriate X matrix, and
- Level 1: Binomial , independently for each Level 2: Beta , independently for each
- Spatial processes and statistical modelling
- Sensitivity of Inference in Forensic Genetics to
- Spatial processes and statistical modelling
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- Contribution to discussion of paper by Brooks, Giudici and Roberts RSS Ordinary meeting, 30 July 2002
- Bayesian Modelling B sheet 4 1. Recall from 1st year probability that
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- Correction to ``On Bayesian analysis of mixtures with an unknown number of components''
- Modelling data from single photon emission computed tomography
- Bayesian alignment using hierarchical models, with applications in protein bioinformatics
- MATH11400 Statistics 1 201011 Homepage http://www.stats.bris.ac.uk/%7Emapjg/Teach/Stats1/
- BCCS 2008/09: Graphical models and complex stochastic systems: Exercises 4
- Gibbs sampling, continued from
- MATH11400 Statistics 1 2009-10 Homepage http://www.stats.bris.ac.uk/mapjg/Teach/Stats1/
- Detailed balance for Metropolis-Hastings We need to check that
- Bayesian Modelling B sheet 2 1. Prove properties 2 and 3 on slide 17, in the discrete case.
- A more general directed acyclic graph (DAG). It
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- Colouring and breaking sticks: random distributions and
- Identifying influential model choices in Bayesian hierarchical models
- Enumerating the decomposable neighbours of a decomposable graph under
- Genetic Epidemiology (2008) Inference From Genome-Wide Association Studies Using a Novel
- A Bayesian Hierarchical Model for Photometric Redshifts Merrilee Hurn
- Bayesian Model Based Clustering Procedures John W. Lau
- Further Hidden Markov Model Cryptanalysis P.J. Green1
- Contribution to discussion of paper by Lauritzen and Richardson RSS Ordinary meeting, 12 December 2001
- Bayesian Variable Selection and the Swendsen-Wang Algorithm
- Hidden Markov Models and Disease Mapping Peter J. Green and Sylvia Richardson
- Modelling Heterogeneity With and Without the Dirichlet Process
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- Reversible jump MCMC Peter J. Green and David I. Hastie
- Markov chain Monte Carlo in action: a tutorial Peter J. Green
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- BCCS 2008/09: Graphical models and complex stochastic systems: Take-home open-book test sketch solutions and comments
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- BCCS 2008/09: Graphical models and complex stochastic systems: Lecture 5: Hierarchical models
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- Verification of assumptions of ANOVA Here we spell out the checking of assumptions of the
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- Linear Models sheet 3 1. The Gauss-Markov theorem shows that in general, least squares estimators have smaller
- More on graphical modelling Directed acyclic graphs are a natural representation of the way we
- 2. Independent parameters: All the 's are entirely unrelated, in which case the results from each unit can be analysed
- Modelling the excess variation Perhaps we could modify our beta-binomial model to allow for a
- 4. Computational Bayesian Inference Bayesian inference centres around the distribution
- How do we sample from the posterior? In general, we want samples from the joint posterior
- Detailed balance The key idea in most practical MCMC methods is reversibility or
- > # enter data > y<-c(5,1,5,14,3,19,1,1,4,22)
- > # enter data > y<-c(5,1,5,14,3,19,1,1,4,22)
- Section 3.2 (SAVIAH) To examine sensitivity of the local critique plots of the SAVIAH application to minor modifications or
- Bayesian alignment using hierarchical models, with applications in protein bioinformatics
- MATH11400 Statistics 1 201011 Homepage http://www.stats.bris.ac.uk/%7Emapjg/Teach/Stats1/
- MATH11400 Statistics 1 201011 Homepage http://www.stats.bris.ac.uk/%7Emapjg/Teach/Stats1/
- MATH11400 Statistics 1 201011 Homepage http://www.stats.bris.ac.uk/%7Emapjg/Teach/Stats1/
- MATH11400 Statistics 1 201011 Homepage http://www.stats.bris.ac.uk/%7Emapjg/Teach/Stats1/
- MATH11400 Statistics 1 201011 Homepage http://www.stats.bris.ac.uk/%7Emapjg/Teach/Stats1/
- MATH11400 Statistics 1 201011 Homepage http://www.stats.bris.ac.uk/%7Emapjg/Teach/Stats1/
- BCCS 2008/09: Graphical models and complex stochastic systems: Exercises 3
- BCCS 2008/09: Graphical models and complex stochastic systems: Take-home open-book test
- MATH11400 Statistics 1 201011 Homepage http://www.stats.bris.ac.uk/%7Emapjg/Teach/Stats1/
- Decomposable graphical gaussian model determination
- MATH11400 Statistics 1 201011 Homepage http://www.stats.bris.ac.uk/%7Emapjg/Teach/Stats1/
- AST Nonparametric regression 20012 Using cubic smoothing splines within an iterative loop
- MATH11400 Statistics 1 201011 Homepage http://www.stats.bris.ac.uk/%7Emapjg/Teach/Stats1/
- 6. Factorial experiments This section is concerned with the situation where all of
- BCCS 2008/09: Graphical models and complex stochastic systems: Lecture 7: Hidden Markov models and State space models
- RSS ordinary meeting 15 December 1998 ---contribution to discussion of paper by Verbyla, et al.
- BCCS 2008/09: Graphical models and complex stochastic systems: Exercises 2
- MATH11400 Statistics 1 201011 Homepage http://www.stats.bris.ac.uk/%7Emapjg/Teach/Stats1/
- Contribution to discussion of paper by Spiegelhalter, Best, Carlin and van der Linde
- BCCS 2008/09: Graphical models and complex stochastic systems: Lecture 9: Markov chain Monte Carlo
- Supplementary materials for this article are available at http://pubs.amstat.org/toc/jcgs/18/3. Alignment of Multiple Configurations
- MATH11400 Statistics 1 201011 Homepage http://www.stats.bris.ac.uk/%7Emapjg/Teach/Stats1/
- Linear Models: sketch solutions to 2005 examination 1. (a) Observations are unbiased, have equal variance 2 and are uncorrelated. [5 marks]
- Spring term 2006 Bayesian Modelling B
- BCCS 2008/09: Graphical models and complex stochastic systems: Lecture 10: Bayesian model choice
- As in the one-way analysis, the choice of constraints means that all cross-terms cancel when the square is
- Markov chain Monte Carlo in action: a tutorial Peter J. Green
- Modelling spatially correlated data via mixtures: a Bayesian approach
- Bayesian Model Based Clustering Procedures John W. Lau
- Enumerating the junction trees of a decomposable graph
- Comments on `Bayesian Backfitting' by Hastie and Tibshirani
- Example: mortality in infant cardiac surgery, ctd. i.e.
- Autumn term 2005 Linear models
- J. R. Statist. Soc. B (1997) 59, No. 4, pp. 731-792
- Comment on paper: ``Bayesian analysis of agricultural field experiments''
- Bayesian analysis of Poisson mixtures Val'erie Viallefont \Lambda
- The Annals of Applied Statistics 2009, Vol. 3, No. 2, 731763
- MATH11400 Statistics 1 201011 Homepage http://www.stats.bris.ac.uk/%7Emapjg/Teach/Stats1/
- Linear Models sheet 4 1. Look again at the rubber data; use anova() to form an appropriate ANOVA table,
- EXAMINERS REPORT & SOLUTIONS STATISTICS 1 (MATH 11400) May-June 2009 Examiners Report
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- UNIVERSITY OF BRISTOL Examination for the Degrees of B.Sc. and M.Sci. (Level 1)
- UNIVERSITY OF BRISTOL Examination for the Degrees of B.Sc. and M.Sci. (Level 1)
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- Trans-dimensional Markov chain Monte Carlo Peter J. Green
- ?) Board of the Foundationof the ScandinavianJournalof Statistics 1998. Publishedby Blackwell PublishersLtd, 108 Cowley Road, Oxford OX4 1JF,UK and 350 Main Street, Malden, MA 02148, USA. Vol 25: 483-502, 1998