Applied Bayesian modelling for ecologists and epidemiologists (ABME03)
Delivered by Dr. Matt Denwood and Prof. Jason Matthiopoulos
This 6 day course will run from 20th – 25th November 2017 at SCENE
field station, Loch Lomond national park, Scotland.
This application-driven course will provide a founding in the basic
theory & practice of Bayesian statistics, with a focus on MCMC modelling
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.
Course content is as follows
• Revision of likelihoods using full likelihood profiles and an
introduction to the theory of Bayesian statistics.
o Probability and likelihood
o Conditional, joint and total probability, independence, Baye’s law
o Probability distributions
o Uniform, Bernoulli, Binomial, Poisson, Gamma, Beta and Normal
distributions – their range, parameters and common usesoLikelihood and
parameter estimation by maximum likelihood
o Numerical likelihood profiles and maximum likelihood
• Introduction to Bayesian statistics
o Relationship between prior, likelihood & posterior distributions
o Summarising a posterior distribution; The philosophical differences
between frequentist & Bayesian statistics, & the practical implications
o Applying Bayes’ theorem to discrete & continuous data for common
data types given different priors
o Building a posterior profile for a given dataset, & compare the effect
of different priors for the same data
• 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.
o Introduction to MCMC.
o The curse of dimensionality & the advantages of MCMC sampling to
determine a posterior distribution.
o Monte Carlo integration, standard error, & summarising samples from
posterior distributions in R .
o Writing a Metropolis algorithm & generating a posterior distribution
for a simple problem using MCMC.
o Markov chains, autocorrelation & convergence.
o Definition of a Markov chain.
o Autocorrelation, effective sample size and Monte Carlo error.
o The concept of a stationary distribution and burning.
o Requirement for convergence diagnostics, and common statistics for
o Adapting an existing Metropolis algorithm to use two chains, &
assessing the effect of the sampling distribution on the
o Introduction to BUGS & running simple models in JAGS.
o Introduction to the BUGS language & how a BUGS model is translated to
an MCMC sampler during compilation.
o The difference between deterministic & stochastic nodes, & the
contribution of priors & the likelihood.
o Running, extending & interpreting the output of simple JAGS models
from within R using the runjags interface.
• This day will focus on the 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.
o Using JAGS for common problems in biology.
o Understanding and generating code for basic generalised linear mixed
models in JAGS.
o Syntax for quadratic terms and interaction terms in JAGS.
o Essential fitting tips and model selection.
o The need for minimal cross-correlation and independence between
parameters and how to design a model with these properties.
o The practical methods and implications of minimizing Monte Carlo error
and autocorrelation, including thinning.
o Interpreting the DIC for nested models, and understanding the
limitations of how this is calculated.
o Other methods of model selection and where these might be more useful
o Most commonly used methods Rationale and use for fixed threshold,
ABGD, K/theta, PTP, GMYC with computer practicals.
o Other methods, Haplowebs, bGMYC, etc. with computer practicals
• Day 4 will focus on 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.
o General guidance for model specification.
o The flexibility of the BUGS language and MCMC methods.
o The difference between informative and diffuse priors.
o Conjugate priors and how they can be used.
o Gibbs sampling.
o State space models.
o Hierarchical and state space models.
o Latent class and mixture models.
o Conceptual application to animal movement.
o Hands-on application to population biology.
o Conceptual application to epidemiology
• Day 5 will give some additional practical guidance for the use of
Bayesian methods in practice, and finish with a brief overview of more
advanced Bayesian tools such as inla and stan.
o Additional Bayesian methods.
o Understand the usefulness of conjugate priors for robust analysis of
proportions (Binomial and Multinomial data).
o Be aware of some methods of prior elicitation.
o Advanced Bayesian tools.
o Strengths and weaknesses of Integrated Nested Laplace Approximation
(INLA) compared to BUGS.
o Strengths and weaknesses of Stan compared to BUGS
• Round table discussions and problem solving with final Q and A round
table discussion and problem solving with final Q and A.
o The final day will consist of round table discussions, the class will
be split in to smaller groups to discuss set topics/problems. This will
include participants own data where possible. After an early lunch there
will be a general question and answer time until approx. 2pm as a whole
group before transport to Balloch train station.
There will be a 15 minute morning coffee break, an hour for lunch, and
a15 minute afternoon coffee break. We keep the timing of these flexible
depending how the course advances. Breakfast is from 08:00-08:45 and
dinner is at 18:00 each day.
Please email any inquiries to [hidden email] or visit our
Please feel free to distribute this material anywhere you feel is
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