[R-sig-eco] FINAL CALL FOR Applied Bayesian modelling for ecologists and epidemiologists (ABME04)

[Oliver Hooker]

FINAL CALL FOR Applied Bayesian modelling for ecologists and
epidemiologists (ABME04)

https://www.prstatistics.com/course/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.

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 – 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.

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.PSstatistics.com (Behaviour and cognition)


1.    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/

2.    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/

3.    October 23rd – 25th 2018
INTRODUCTIUON TO R (This is a private ‘in-house’ course)
London, England, Dr William Hoppitt

4.    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/

5.    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/

6.    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/

7.    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/

8.    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/

9.    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/

10.    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/

11.    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/

12.    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/

13.    February  4th – 8th 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/

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

15.    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/

16.    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/

17.    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/

18.    March 18th – 22nd 2019
INTRODUCTION TO STATISTICS AND R FOR EVERYONE (IRFE01)
Crete, Greece, Dr Aristides (Aris) Moustakas
https://www.prstatistics.com/course/introduction-to-statistics-and-r-for-
anyone-irfe01/

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

20.    April 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/

21.    April 1st – 5th 2019
INDIVIDUAL BASED MODELS FOR ECOLOGSITS (IBME01)
Glasgow Scotland, Dr Aristides (Aris) Moustakas
Link to follow

22.    April 8th – 12th 2019
MACHINE LEARNING
Glasgow Scotland, Dr Aristides (Aris) Moustakas
https://www.prstatistics.com/course/machine-learning-using-r-mlur01/

23.    April 8th – 12th 2019
Spatial modelling, analysis and statistical inference of genomic data
(SMAG01)
Crete, Greece, Dr Matt Fitzpatrick
https://www.prstatistics.com/course/spatial-modelling-analysis-and-
statistical-inference-of-genomic-data-smag01/

24.    May 6th – 10th 2019
MARK RECAPTURE METHODS AND DATA ANALYSIS FOR ECOLOGISTS (MRKR01)
Myuna Bay, Australia, TBC

25.    May 16th – 18th 2019 (please note this a 3-day course from 
Thursday
to Saturday)
Aquatic movement ecology using R (AMER01)
Myuna Bay, Australia, TBC

26.    May 16th – 19th 2019 (please note this a 4-day course from 
Thursday
to Monday)
Introduction to R for everyone (IRFE02)
Myuna Bay, Australia, Dr Aristides (Aris) Moustakas

27.    May 20th – 24th 2019
MODEL BASE MULTIVARIATE ANALYSIS OF ABUNDANCE DATA USING R (MBMV03)
Myuna Bay, Australia, Prof. David Warton
https://www.prstatistics.com/course/model-based-multivariate-analysis-of-
abundance-data-using-r-mbmv03/

28.    May 21st – 24th 2019
A statistical tool box for ecologists (STBE01
Myuna Bay, Australia, Dr Aristides (Aris) Moustakas

29.    June 10th – 14th 2019
STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR (SIMM04)
Glasgow, Scotland, Dr. Andrew Parnell, Dr. Andrew Jackson
www.prstatistics.com/course/stable-isotope-mixing-models-using-r-simm04/

30.    June 17th – 21st 2019
INTRODUCTION TO PYTHON FOR BIOLOGISTS (IPYB06)
Glasgow, Scotland, Dr. Martin Jones
http://www.prinformatics.com/course/introduction-to-python-for-biologists-
ipyb06/

31.    June 24th – 28th 2019
ADVANCED PYTHON FOR BIOLOGISTS (APYB03)
Glasgow, Scotland, Dr. Martin Jones
www.prinformatics.com/course/advanced-python-biologists-apyb03/

32.    July 1st – 5th 2019
DATA VISUALISATION AND MANIPULATION USING PYTHON (DVMP01)
Glasgow, Scotland, Dr. Martin Jones
http://www.prinformatics.com/course/data-visualisation-and-manipulation-
using-python-dvmp01/

33.    October 7th – 11th 2019
CONSERVATION PLANNING USING PRIORITIZR : FROM THEORY TO PRACTICE 
(PRTZ01)
Crete, Greece, Dr Richard Schuster and Nina Morell
https://www.prstatistics.com/course/conservation-planning-using-prioritizr-
from-theory-to-practice-prtz01/

34.    October 21st – 25th 2019
A COMPLETE GUIDE TO MIXED MODELS (INCLUDING TEMPORAL AND SPATIAL
AUTOCORRELATION) (MMTS01)
Crete, Greece, Dr Aristides (Aris) Moustakas
https://www.prstatistics.com/course/a-complete-guide-to-mixed-models-
including-temporal-and-spatial-autocorrelation-mmts01/

-- 
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

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