[R-sig-Epi] Applied Bayesian modelling for ecologists and epidemiologists

Wed Aug 22 17:37:08 CEST 2018

Applied Bayesian modelling for ecologists and epidemiologists (ABME04)

This course will be delivered by Matt Denwood in Glasgow City Centre 
from 15th - 19th October 2018.

https://www.prstatistics.com/course/applied-bayesian-modelling-for-ecologists-and-epidemiologists-abme04/

Please feel free to share anywhere you see fit.

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.

Intended Audience
Research postgraduates, practicing academics and primary investigators 
in ecology and epidemiology and professionals in government and 
industry.

Monday 15th – Classes from 09:00 to 17:00
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 09:00 to 17:00

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 09:00 to 17:00
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 09:00 to 17:00
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 09:00 to 17:00
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 oliverhooker using 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)

1.	October 1st – 5th
TIME SERIES MODELS FOR ECOLOGISTS (TSME02)
Glasgow, Dr Andrew Parnell
https://www.prstatistics.com/course/time-series-models-foe-ecologists-tsme02/

2.	October 1st – 5th 2018
INTRODUCTION TO LINUX WORKFLOWS FOR BIOLOGISTS (IBUL03)
Glasgow, Scotland, Dr. Martin Jones
https://www.prinformatics.com/course/introduction-to-linux-workflows-for-biologists-ibul03/

3.	October 8th – 12th 2018
INTRODUCTION TO FREQUENTIST AND BAYESIAN MIXED (HIERARCHICAL) MODELS 
(IFBM01)
Glasgow, Scotland, Dr Andrew Parnell
https://www.psstatistics.com/course/introduction-to-frequentis-and-bayesian-mixed-models-ifbm01/

4.	October 15th – 19th 2018
APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS (ABME04)
Glasgow, Scotland, Dr. Matt Denwood, Emma Howard
http://www.prstatistics.com/course/applied-bayesian-modelling-ecologists-epidemiologists-abme04/

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
https://www.prstatistics.com/course/introduction-to-statistics-and-r-for-biologists-irfb02/

7.	October 29th – November 2nd 2018
INTRODUCTION TO BIOINFORMATICS FOR DNA AND RNA SEQUENCE ANALYSIS 
(IBDR01)
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/

8.	November 5th – 8th  2018
PHYLOGENETIC COMPARATIVE METHODS FOR STUDYING DIVERSIFICATION AND 
PHENOTYPIC EVOLUTION (PCME01)
Glasgow, Scotland, Dr. Antigoni Kaliontzopoulou
https://www.prstatistics.com/course/phylogenetic-comparative-methods-for-studying-diversification-and-phenotypic-evolution-pcme01/

9.	November 19th – 23rd  2018
STRUCTUAL EQUATION MODELLING FOR ECOLOGISTS AND EVOLUTIONARY BIOLOGISTS 
(SEMR02)
Glasgow, Scotland, Dr. Jonathan Lefcheck
https://www.prstatistics.com/course/structural-equation-modelling-for-ecologists-and-evolutionary-biologists-semr02/

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
http://www.prstatistics.com/course/functional-ecology-from-organism-to-ecosystem-theory-and-computation-feer01/

11.	December 3rd – 7th 2018
INTRODUCTION TO BAYESIAN DATA ANALYSIS FOR SOCIAL AND BEHAVIOURAL 
SCIENCES USING R AND STAN (BDRS01)
Glasgow, Dr. Mark Andrews
https://www.psstatistics.com/course/introduction-to-bayesian-data-analysis-for-social-and-behavioural-sciences-using-r-and-stan-bdrs01/

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
https://www.psstatistics.com/course/statistical-modelling-of-time-to-event-data-using-survival-analysis-tted01/

13.	January 21st – 25th 2019
ADVANCING IN STATISTICAL MODELLING USING R (ADVR08)
Glasgow, Scotland, Dr. Luc Bussiere, Dr. Tom Houslay
http://www.prstatistics.com/course/advancing-statistical-modelling-using-r-advr08/

14.	January 28th–  February 1st 2019
AQUATIC ACOUSTIC TELEMETRY DATA ANALYSIS AND SURVEY DESIGN
Glasgow, Scotland, VEMCO staff and affiliates
https://www.prstatistics.com/course/aquatic-acoustic-telemetry-data-analysis-atda01/

15.	4th – 8th February 2019
DESIGNING RELIABLE AND EFFICIENT EXPERIMENTS FOR SOCIAL SCIENCES 
(DRES01)
Glasgow, Scotland, Dr. Daniel Lakens
https://www.psstatistics.com/course/designing-reliable-and-effecient-experiments-for-social-sciences-dres01/

16.	February 11th – 15th 2019
REPRODUCIBLE DATA SCIEDNCE FOR POPULATION GENETICS
Glasgow, Scotland, Dr. Thibaut Jombart, Dr. Zhain Kamvar
https://www.prstatistics.com/course/reproducible-data-science-for-population-genetics-rdpg02/

17.	25th February – 1st March 2019
MOVEMENT ECOLOGY (MOVE02)
Margam Discovery Centre, Wales, Dr. Luca Borger, Prof. Ronny Wilson, Dr 
Jonathan Potts
https://www.prstatistics.com/course/movement-ecology-move02/

18.	March 4th – 8th 2019
BIOACUSTIC DATA ANALYSIS
Glasgow, Scotland, Dr. Paul Howden-Leach
https://www.prstatistics.com/course/bioacoustics-for-ecologists-hardware-survey-design-and-data-analysis-biac01/

19.	March 11th – 15th  2019
ECOLOGICAL NICHE MODELLING USING R (ENMR03)
Glasgow, Scotland, Dr. Neftali Sillero
http://www.prstatistics.com/course/ecological-niche-modelling-using-r-enmr03/

20.	MARCH 18TH – 22ND 2019
INRODUCTION TO R FOR BIOMEDICAL SCIENCES (IRBM01)
Crete, Greece, Dr Aristides (Aris) Moustakas
Link to follow soon

21.	March 25th – 29th 2019
LANDSCAPE GENETIC/GENOMIC DATA ANALYSIS USING R (LNDG03)
Glasgow, Scotland, Prof. Rodney Dyer
http://www.prstatistics.com/course/landscape-genetic-data-analysis-using-r-lndg03/

22.	A pril 1st – 5th 2019
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-ipsy02/

23.	April 8th – 12th
MACHINE LEARNING
Glasgow Scotland, Dr Aristides (Aris) Moustakas
https://www.prstatistics.com/course/machine-learning-using-r-mlur01/

-- 
Oliver Hooker PhD.
PR statistics

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

prstatistics.com
facebook.com/prstatistics/
twitter.com/PRstatistics
groups.google.com/d/forum/pr-statistics-post-course-forum
prstatistics.com/organiser/oliver-hooker/

6 Hope Park Crescent
Edinburgh
EH8 9NA

+44 (0) 7966500340