Fishery Population Analysis (FPA)

The next lecture will be given on May 29, 2019

Overview of this class

Time: Wed 10:30 - 12am

Location: 8-704

Objectives:

  • Students will learn statistical methods used in fisheries stock assessment and management.
  • The course will also cover the basics of programming in R, TMB and stan.

Grading:

Grading is based on the level of the understanding, reports and presentation in the class.

Schedule:

Lecture Date Time Subject
1 10-Apr-19 AM1030 Course introduction
2 29-May-19 AM0930-AM1015 R basics (R, Rstudio, Rmarkdown)
3 29-May-19 AM1015-AM1100 Expression of stochastic events and data using probabilistic and statistical models
4 5-Jun-19 AM1000-AM1100 Statistical inference (I) maximum likelihood estimation
5 5-Jun-19 AM1100-AM1230 Statistical inference (II) likelihood ratio test and model selection
6 10-Jul-19 AM1000-AM1100 Statistical inference (III) Bayesian methods
7 12-Jun-19 AM1000-AM1100 Regression models (I) linear and nonlinear models
8 12-Jun-19 AM1100-AM1230 Regression models (II) generalized linear and additive models
9 10-Jul-19 AM1100-AM1130 TMB basics
10 10-Jul-19 AM1130-AM1230 Stan basics
11 10-Jul-19 PM0300-PM0400 Advanced models (1) State-space and hierarchical models
12 10-Jul-19 PM0400-PM0500 Advanced models (2) Time series and spatial models
13 10-Jul-19 PM0500-PM0600 Advanced models (3) Spatio-temporal models
14 12-Jul-19 AM1030-AM1130 Project using R/TMB/stan
15 12-Jul-19 AM1130-AM1230 Project presentation

Information:

The course is basically conducted in English, but additional illustration in Japanese will be available upon request. We will use the software “R” etc.. Beginners of “R” are welcomed.

The course organization on GitHub: https://github.com/ToshihideKitakado/FPA2019

The course website: https://toshihidekitakado.github.io/FPA2019/

Placeholder: