Applied Bayesian modelling for ecologists and epidemiologists (ABME04)
This course will be delivered by Prof. Matt Denwood in Glasgow city centre form the 15th - 19th October 2018.
This application-driven course will provide a founding in the basic theory & practice of Bayesian statistics, with a focus on MCMC modeling for ecological & epidemiological problems. Starting from a refresher on probability & likelihood, the course will take students all the way to cutting-edge applications such as state-space population modelling & spatial point-process modelling. By the end of the week, you should have a basic understanding of how common MCMC samplers work and how to program them, and have practical experience with the BUGS language for common ecological and epidemiological models. The experience gained will be a sufficient foundation enabling you to understand current papers using Bayesian methods, carry out simple Bayesian analyses on your own data and springboard into more elaborate applications such as dynamical, spatial and hierarchical modelling.
Monday 15th – Classes from 09:30 to 17:30
Module 1: Revision of likelihoods using full likelihood profiles and an introduction to the theory of Bayesian statistics. Probability and likelihood. Conditional, joint and total probability, independence, Baye’s law. Probability distributions. Uniform, Bernoulli, Binomial, Poisson, Gamma, Beta and Normal distributions – their range, parameters and common uses of Likelihood and parameter estimation by maximum likelihood. Numerical likelihood profiles and maximum likelihood. Introduction to Bayesian statistics.
Relationship between prior, likelihood & posterior distributions. Summarising a posterior distribution; The philosophical differences between frequentist & Bayesian statistics, & the practical implications of these.
Applying Bayes’ theorem to discrete & continuous data for common data types given different priors. Building a posterior profile for a given dataset, & compare the effect of different priors for the same data.
Tuesday 16th – Classes from 09:30 to 17:30
Module 2: An introduction to the workings of MCMC, and the potential dangers of MCMC inference. Participants will program their own (basic) MCMC sampler to illustrate the concepts and fully understand the strengths and weaknesses of the general approach. The day will end with an introduction to the bugs language.
Introduction to MCMC. The curse of dimensionality & the advantages of MCMC sampling to determine a posterior distribution. Monte Carlo integration, standard error, & summarising samples from posterior distributions in R. Writing a Metropolis algorithm & generating a posterior distribution for a simple problem using MCMC.
Markov chains, autocorrelation & convergence. Definition of a Markov chain. Autocorrelation, effective sample size and Monte Carlo error. The concept of a stationary distribution and burnin. Requirement for convergence diagnostics, and common statistics for assessing convergence. Adapting an existing Metropolis algorithm to use two chains, & assessing the effect of the sampling distribution on the autocorrelation. Introduction to BUGS & running simple models in JAGS. Introduction to the BUGS language & how a BUGS model is translated to an MCMC sampler during compilation. The difference between deterministic & stochastic nodes, & the contribution of priors & the likelihood. Running, extending & interpreting the output of simple JAGS models from within R using the runjags interface.
Wednesday 17th – Classes from 09:30 to 17:30
Module 3: Common models for which jags/bugs would be used in practice, with examples given for different types of model code. All aspects of writing, running, assessing and interpreting these models will be extensively discussed so that participants are able and confident to run similar models on their own. There will be a particularly heavy focus on practical sessions during this day. The day will finish with a discussion of how to assess the fit of mcmc models using the deviance information criterion (dic) and other methods. Using JAGS for common problems in biology. Understanding and generating code for basic generalised linear mixed models in JAGS. Syntax for quadratic terms and interaction terms in JAGS.
Essential fitting tips and model selection. The need for minimal cross-correlation and independence between parameters and how to design a model with these properties. The practical methods and implications of minimizing Monte Carlo error and autocorrelation, including thinning. Interpreting the DIC for nested models, and understanding the limitations of how this is calculated. Other methods of model selection and where these might be more useful than DIC. Most commonly used methods Rationale and use for fixed threshold, ABGD, K/theta, PTP, GMYC with computer practicals. Other methods, Haplowebs, bGMYC, etc. with computer practicals.
Thursday 18th – Classes from 09:30 to 17:30
Module 4: The flexibility of MCMC, and precautions required for using MCMC to model commonly encountered datasets. An introduction to conjugate priors and the potential benefits of exploiting gibbs sampling will be given. More complex types of models such as hierarchical models, latent class models, mixture models and state space models will be introduced and discussed. The practical sessions will follow on from day 3.
General guidance for model specification. The flexibility of the BUGS language and MCMC methods. The difference between informative and diffuse priors. Conjugate priors and how they can be used. Gibbs sampling. State space models. Hierarchical and state space models. Latent class and mixture models. Conceptual application to animal movement. Hands-on application to population biology. Conceptual application to epidemiology.
Friday 19th – Classes from 09:30 to 17:30
Module 5: Additional practical guidance for the use of Bayesian methods in practice, and finish with a brief overview of more advanced Bayesian tools such as Integrated Nested Laplace Approximation (INLA) and stan.
Additional Bayesian methods. Understand the usefulness of conjugate priors for robust analysis of proportions (Binomial and Multinomial data). Be aware of some methods of prior elicitation. Advanced Bayesian tools. Strengths and weaknesses of INLA compared to BUGS. Strengths and weaknesses of stan compared to BUGS.
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Check out our sister sites,
www.PRstatistics.com (Ecology and Life Sciences)
www.PRinformatics.com (Bioinformatics and data science)
www.PSstatsistics.com (Behaviour and cognition)
1. October 1st – 5th
TIME SERIES MODELS FOR ECOLOGISTS (TSME02)
Glasgow, Dr Andrew Parnell
2. October 1st – 5th 2018
INTRODUCTION TO LINUX WORKFLOWS FOR BIOLOGISTS (IBUL03)
Glasgow, Scotland, Dr. Martin Jones
3. October 8th – 12th 2018
INTRODUCTION TO FREQUENTIST AND BAYESIAN MIXED (HIERARCHICAL) MODELS (IFBM01)
Glasgow, Scotland, Dr Andrew Parnell
4. October 15th – 19th 2018
APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS (ABME04)
Glasgow, Scotland, Dr. Matt Denwood, Emma Howard
5. October 23rd – 25th 2018
INTRODUCTIUON TO R (This is a private ‘in-house’ course)
London, England, Dr William Hoppitt
6. October 29th – November 2nd 2018
INTRODCUTION TO R AND STATISTICS FOR BIOLOGISTS (IRFB02)
Glasgow, Scotland, Dr. Olivier Gauthier
7. October 29th – November 2nd 2018
INTRODUCTION TO BIOINFORMATICS FOR DNA AND RNA SEQUENCE ANALYSIS (IBDR01)
Glasgow, Scotland, Dr Malachi Griffith, Dr. Obi Griffith
8. November 5th – 8th 2018
PHYLOGENETIC COMPARATIVE METHODS FOR STUDYING DIVERSIFICATION AND PHENOTYPIC EVOLUTION (PCME01)
Glasgow, Scotland, Dr. Antigoni Kaliontzopoulou
9. November 19th – 23rd 2018
STRUCTUAL EQUATION MODELLING FOR ECOLOGISTS AND EVOLUTIONARY BIOLOGISTS (SEMR02)
Glasgow, Scotland, Dr. Jonathan Lefcheck
10. November 26th – 30th 2018
FUNCTIONAL ECOLOGY FROM ORGANISM TO ECOSYSTEM: THEORY AND COMPUTATION (FEER01)
Glasgow, Scotland, Dr. Francesco de Bello, Dr. Lars Götzenberger, Dr. Carlos Carmona
11. December 3rd – 7th 2018
INTRODUCTION TO BAYESIAN DATA ANALYSIS FOR SOCIAL AND BEHAVIOURAL SCIENCES USING R AND STAN (BDRS01)
Glasgow, Dr. Mark Andrews
12. January 21st – 25th 2019
STATISTICAL MODELLING OF TIME-TO-EVENT DATA USING SURVIVAL ANALYSIS: AN INTRODUCTION FOR ANIMAL BEHAVIOURISTS, ECOLOGISTS AND EVOLUTIONARY BIOLOGISTS (TTED01)
Glasgow, Scotland, Dr. Will Hoppitt
13. January 21st – 25th 2019
ADVANCING IN STATISTICAL MODELLING USING R (ADVR08)
Glasgow, Scotland, Dr. Luc Bussiere, Dr. Tom Houslay
14. January 28th– February 1st 2019
AQUATIC ACOUSTIC TELEMETRY DATA ANALYSIS AND SURVEY DESIGN
Glasgow, Scotland, VEMCO staff and affiliates
15. 4th – 8th February 2019
DESIGNING RELIABLE AND EFFICIENT EXPERIMENTS FOR SOCIAL SCIENCES (DRES01)
Glasgow, Scotland, Dr. Daniel Lakens
16. February 11th – 15th 2019
REPRODUCIBLE DATA SCIEDNCE FOR POPULATION GENETICS
Glasgow, Scotland, Dr. Thibaut Jombart, Dr. Zhain Kamvar
17. 25th February – 1st March 2019
MOVEMENT ECOLOGY (MOVE02)
Margam Discovery Centre, Wales, Dr. Luca Borger, Prof. Ronny Wilson, Dr Jonathan Potts
18. March 4th – 8th 2019
BIOACUSTIC DATA ANALYSIS
Glasgow, Scotland, Dr. Paul Howden-Leach
19. March 11th – 15th 2019
ECOLOGICAL NICHE MODELLING USING R (ENMR03)
Glasgow, Scotland, Dr. Neftali Sillero
20. MARCH 18th – 22nd 2019
INTRODUCTION TO STATISTICS AND R FOR EVERYONE (IRFE01)
Crete, Greece, Dr Aristides (Aris) Moustakas
21. March 25th – 29th 2019
LANDSCAPE GENETIC/GENOMIC DATA ANALYSIS USING R (LNDG03)
Glasgow, Scotland, Prof. Rodney Dyer
22. A pril 1st – 5th 2019
INTRODUCTION TO STATISTICAL MODELLING FOR PSYCHOLOGISTS USING R (IPSY01)
Glasgow, Scotland, Dr. Dale Barr, Dr Luc Bussierre
23. April 8th – 12th
Glasgow Scotland, Dr Aristides (Aris) Moustakas
Oliver Hooker PhD.
2018 publications -
Alternative routes to piscivory: Contrasting growth trajectories in brown trout (Salmo trutta) ecotypes exhibiting contrasting life history strategies. Ecology of Freshwater Fish. DOI to follow
Phenotypic and resource use partitioning amongst sympatric lacustrine brown trout, Salmo trutta. Biological Journal of the Linnean Society. DOI 10.1093/biolinnean/bly032
6 Hope Park Crescent
+44 (0) 7966500340
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