Applied Bayesian modelling for ecologists and epidemiologists (ABME04) https://www.prstatistics.com/course/applied-bayesian-modelling-for- ecologists-and-epidemiologists-abme04/
This course will run from the 15th - 19th October 2018 in Glasgow city centre and will be delivered by Dr Matt Denwood. Course Overview: 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 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 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 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 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 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. Email oliverhoo...@prstatistics.com 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) Upcoming courses 1. April 9th – 13th 2018 NETWORK ANAYLSIS FOR ECOLOGISTS USING R (NTWA02 Glasgow, Scotland, Dr. Marco Scotti www.prstatistics.com/course/network-analysis-ecologists-ntwa02/ 2. April 16th – 20th 2018 INTRODUCTION TO STATISTICAL MODELLING FOR PSYCHOLOGISTS USING R (IPSY01) Glasgow, Scotland, Dr. Dale Barr, Dr Luc Bussierre http://www.psstatistics.com/course/introduction-to-statistics-using-r-for- psychologists-ipsy01/ 3. April 23rd – 27th 2018 MULTIVARIATE ANALYSIS OF ECOLOGICAL COMMUNITIES USING THE VEGAN PACKAGE (VGNR01) Glasgow, Scotland, Dr. Peter Solymos, Dr. Guillaume Blanchet www.prstatistics.com/course/multivariate-analysis-of-ecological-communities- in-r-with-the-vegan-package-vgnr01/ 4. April 30th – 4th May 2018 QUANTITATIVE GEOGRAPHIC ECOLOGY: MODELING GENOMES, NICHES, AND COMMUNITIES (QGER01) Glasgow, Scotland, Dr. Dan Warren, Dr. Matt Fitzpatrick www.prstatistics.com/course/quantitative-geographic-ecology-using-r- modelling-genomes-niches-and-communities-qger01/ 5. May 7th – 11th 2018 ADVANCES IN MULTIVARIATE ANALYSIS OF SPATIAL ECOLOGICAL DATA USING R (MVSP02) CANADA (QUEBEC), Prof. Pierre Legendre, Dr. Guillaume Blanchet www.prstatistics.com/course/advances-in-spatial-analysis-of-multivariate- ecological-data-theory-and-practice-mvsp03/ 6. May 14th - 18th 2018 INTRODUCTION TO MIXED (HIERARCHICAL) MODELS FOR BIOLOGISTS (IMBR01) CANADA (QUEBEC), Prof Subhash Lele www.prstatistics.com/course/introduction-to-mixed-hierarchical-models-for- biologists-using-r-imbr01/ 7. May 21st - 25th 2018 INTRODUCTION TO PYTHON FOR BIOLOGISTS (IPYB05) SCENE, Scotland, Dr. Martin Jones http://www.prinformatics.com/course/introduction-to-python-for-biologists- ipyb05/ 8. May 21st - 25th 2018 INTRODUCTION TO REMOTE SENISNG AND GIS FOR ECOLOGICAL APPLICATIONS (IRMS01) Glasgow, Scotland, Prof. Duccio Rocchini, Dr. Luca Delucchi www.prinformatics.com/course/introduction-to-remote-sensing-and-gis-for- ecological-applications-irms01/ 9. May 28th – 31st 2018 STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR (SIMM04) CANADA (QUEBEC) Dr. Andrew Parnell, Dr. Andrew Jackson www.prstatistics.com/course/stable-isotope-mixing-models-using-r-simm04/ 10. May 28th – June 1st 2018 ADVANCED PYTHON FOR BIOLOGISTS (APYB02) SCENE, Scotland, Dr. Martin Jones www.prinformatics.com/course/advanced-python-biologists-apyb02/ 11. June 12th - 15th 2018 SPECIES DISTRIBUTION MODELLING (DBMR01) Myuna Bay sport and recreation, Australia, Prof. Jane Elith, Dr. Gurutzeta Guillera www.prstatistics.com/course/species-distribution-models-using-r-sdmr01/ 12. June 18th – 22nd 2018 STRUCTURAL EQUATION MODELLING FOR ECOLOGISTS AND EVOLUTIONARY BIOLOGISTS USING R (SEMR02) Myuna Bay sport and recreation, Australia, Dr. Jon Lefcheck www.prstatistics.com/course/structural-equation-modelling-for-ecologists- and-evolutionary-biologists-semr02/ 13. June 25th – 29th 2018 SPECIES DISTRIBUTION/OCCUPANCY MODELLING USING R (OCCU01) Glasgow, Scotland, Dr. Darryl McKenzie www.prstatistics.com/course/species-distributionoccupancy-modelling-using-r- occu01/ 14. July 2nd - 5th 2018 SOCIAL NETWORK ANALYSIS FOR BEHAVIOURAL SCIENTISTS USING R (SNAR01) Glasgow, Scotland, Prof James Curley http://www.psstatistics.com/course/social-network-analysis-for-behavioral- scientists-snar01/ 15. July 8th – 12th 2018 MODEL BASE MULTIVARIATE ANALYSIS OF ABUNDANCE DATA USING R (MBMV02) Glasgow, Scotland, Prof David Warton www.prstatistics.com/course/model-base-multivariate-analysis-of-abundance- data-using-r-mbmv02/ 16. July 16th – 20th 2018 PRECISION MEDICINE BIOINFORMATICS: FROM RAW GENOME AND TRANSCRIPTOME DATA TO CLINICAL INTERPRETATION (PMBI01) Glasgow, Scotland, Dr Malachi Griffith, Dr. Obi Griffith www.prinformatics.com/course/precision-medicine-bioinformatics-from-raw- genome-and-transcriptome-data-to-clinical-interpretation-pmbi01/ 17. July 23rd – 27th 2018 EUKARYOTIC METABARCODING (EUKB01) Glasgow, Scotland, Dr. Owen Wangensteen http://www.prinformatics.com/course/eukaryotic-metabarcoding-eukb01/ 18. October 8th – 12th 2018 INTRODUCTION TO SPATIAL ANALYSIS OF ECOLOGICAL DATA USING R (ISAE01) Glasgow, Scotland, Prof. Subhash Lele https://www.prstatistics.com/course/introduction-to-spatial-analysis-of- ecological-data-using-r-isae01/ 19. October 15th – 19th 2018 APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS (ABME Glasgow, Scotland, Dr. Matt Denwood, Emma Howard http://www.prstatistics.com/course/applied-bayesian-modelling-ecologists- epidemiologists-abme04/ 20. October 29th – November 2nd 2018 PHYLOGENETIC COMPARATIVE METHODS FOR STUDYING DIVERSIFICATION AND PHENOTYPIC EVOLUTION (PCME01) Glasgow, Scotland, Prof. Subhash Lele Dr. Antigoni Kaliontzopoulou https://www.prstatistics.com/course/phylogenetic-comparative-methods-for- studying-diversification-and-phenotypic-evolution-pcme01/ 21. November 26th – 30th 2018 FUNCTIONAL ECOLOGY FROM ORGANISM TO ECOSYSTEM: THEORY AND COMPUTATION (FEER Glasgow, Scotland, Dr. Francesco de Bello, Dr. Lars Götzenberger, Dr. Carlos Carmona http://www.prstatistics.com/course/functional-ecology-from-organism-to- ecosystem-theory-and-computation-feer01/ 22. February 2018 TBC MOVEMENT ECOLOGY (MOVE02) Margam Discovery Centre, Wales, Dr Luca Borger, Dr Ronny Wilson, Dr Jonathan Potts www.prstatistics.com/course/movement-ecology-move01/
