Delivering ecology based courses and workshops
Course: Applied Bayesian modelling for ecologists
Instructors: Dr. Matt Denwood and Prof. Jason Matthiopoulos.
Coordinator: Oliver Hooker.
Date: TBC - email for detials.
Cost: TBC - email for detials.
Availability: 30 places total.
Venue: TBC - email for detials.
Duration: 6 days, approximately 8 teaching hours per day.
Registration: Please send course requests to email@example.com stating the course title and if you would be interested in an all inclusive option after which you will be sent a registration form and invoice.
Course details: Starting from a refresher on probability & likelihood, the course will take students all the way to cutting-edge applications such as state-space population modeling & spatial point-process modeling. 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 modeling.
Day 1: REVISION OF LIKELIHOODS USING FULL LIKELIHOOD PROFILES AND AN INTRODUCTION TO THE THEORY OF BAYESIAN STATISTICS
Day 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.
Day 3: 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.
Day 4: 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.
Day 5: 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.
Day 6: ROUND TABLE DISCUSSIONS AND PROBLEM SOLVING WITH FINAL Q and ARound table discussion and problem solving with final Q and A
Outcomes: By the end of this course you will be able to:
Assumed computer background: At entry you should make sure that you have a working knowledge of:
Equipment and software requirements: There is a computer based practical session every day, so you will need to bring a laptop/personal computer pre-loaded with the following (free) software (don’t assume that you will have internet access during the course):