Note: the following videos are only digest versions for students’ preparation before the remote sessions.

Lecture 1. Course introduction (May 13, 2020)
  • 10:30-11:00 Course overview
  • Test use of R, Rstudio and Rmarkdown using Lecture 2 material
Lecture 2. R basics (R, Rstudio, Rmarkdown) (May 20, 2020)
  • Trial for writing a simple Rmarkdown report for better preparation of submission of reports but without going into details of the analysis

Handout (pdf) Lab (zip)

Lecture 3. Expression of stochastic events and data using probabilistic and statistical models (May 20, 2020)
  • To address how we can account for various kinds of uncertainty using statistical models

Video (mp4, 40MB) Handout (pdf) Lab (zip)

Lecture 4 & 5. Maximum likelihood estimation - intrdouction and uncertainty (May 27, 2020)
  • Introduction of ML estimation
  • Examples for occupancy modelling

Video (mp4, 33MB) Handout (pdf) Handout_suppl (pdf)

Lecture 6. Maximum likelihood estimation - A fictitious paper - (Jun 3, 2020)
  • A story of likelihood inference using a fictitious paper with a subeject of school size estimation
  • Likelihood estimation, evaluation of standard error, likelihood ratio test and model selection
  • I will explain the profile likelihood when introducing TMB

Video (mp4, 42MB) Handout (pdf)

Lecture 7. Maximum likelihood estimation - TMB - (Jun 10, 2020)
  • Introduction of Template Model Builder (TMB)

Video (mp4, 33MB) Handout (pdf) Lab (zip)

Lecture 8. Maximum likelihood estimation - TMB continued - (Jun 17 and 24, 2020)
  • Use of Template Model Builder (TMB) for ramdom-effect models

Video (mp4, to come) Handout for random effects(pdf)

Lecture 11a. State-space modelling with TMB (August 1, 2020)
  • Use of Template Model Builder (TMB) for state-space models

Video (mp4, to come) Handout for state-space(pdf)

Lecture 12. Several regression models (July 1, 2020)
  • Linear regression
  • Nonlinear regression
  • Generalized linear model
  • Generalized linear mixed-effect model
  • Additive model
  • Generalized additive model
  • Spatial modelling using GAM

Handout for regression(pdf)

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