# [ECOLOG-L] Applied Bayesian modelling for ecologists and epidemiologists

```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 &amp;
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
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

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/
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