33. MCMC
• Algorithm for exploring parameter space
1.Pick a starting point
2.Propose a move
34. MCMC
• Algorithm for exploring parameter space
1.Pick a starting point
2.Propose a move
3.Accept or decline move based on
probability
35. MCMC
• Algorithm for exploring parameter space
1.Pick a starting point
2.Propose a move
3.Accept or decline move based on
probability
• Time spent at each point approximates
parameter distribution
36. MCMC
• Algorithm for exploring parameter space
1.Pick a starting point
2.Propose a move
3.Accept or decline move based on
probability
• Time spent at each point approximates
parameter distribution
• E.g. Metropolis-Hastings, Gibbs sampling
43. Many benefits to this
approach
• Simultaneously estimate parameters
• …as well as parameter relationships
• “Borrow” strength across studies
• Model comparison
46. Bayesian stats can do
most things
frequentist, but…
• Many simple models don‟t gain much
• Better do something „boring‟ well than
something exciting poorly
47. Bayesian stats can do
most things
frequentist, but…
• Many simple models don‟t gain much
• Better do something „boring‟ well than
something exciting poorly
• Don‟t be this guy
48. DO use Bayesian
methods if
• You have a complex model with many
interacting parameters
• You have „messy‟ data
• You don‟t want to make assumptions
about distributions
49. In Conclusion
• Bayesian methods are powerful tools
for ecological research
• Like most things statistical, they are
no substitute for thinking
• They are here to stay, and you should
at least be familiar with them
50. Great, I want to learn
more!
JAGS
(Just Another Gibbs Sampler)
