A biref introduction to Bayesian Statistics
35
Bayesian inference for complex models
Intro to Statistical Modeling
Welina mai!
Introductory material
1
Data we will work with
2
CARE and FAIR principles
3
R refresher
4
Dynamic documents with
quarto
5
Managing code with git and GitHub
6
CARE and FAIR in presenting data with dynamic documents
Probability
7
Introduction to probability
8
Probability distributions in R
9
Simulating probabilistic data
Introducing the method of maximum likelihood
10
Introducing likelihood
11
Estimating probability of occurrence
12
Estimating the parameters of a normal distribution
Linear models
13
How to make a model
14
Simple linear models
15
Linear models and least squares
16
What are random effects?
17
Working with mixed effects models in R
18
More mixed effects models in R
19
The Binomial Generalized Linear Model
20
Building Binomial Generalized Linear Models
21
The Poisson Generalized Linear Model
22
Building Poisson Generalized Linear Models
23
Overdispersion and the Negative Binomial GLM
Significance testing and model selection
24
Likelihood ratio test and confidence intervals
25
Applying the Likelihood Ratio Test
26
Constructing confidence intervals
27
Null hypothesis testing considerations and connections
28
Sample size and power analysis
29
Model Comparison and Averaging
30
Comparing models with AIC
31
Averaging models with AIC
A biref introduction to Bayesian Statistics
32
Intro to Bayesian Statistics
33
Basics of Bayesian inference
34
Sample size considerations and posterior predictive checks
35
Bayesian inference for complex models
36
Species abundance: a surprisingly complex model
Table of contents
35.1
Slides
Code Links
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A biref introduction to Bayesian Statistics
35
Bayesian inference for complex models
35
Bayesian inference for complex models
35.1
Slides
34
Sample size considerations and posterior predictive checks
36
Species abundance: a surprisingly complex model