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Model Fitting II (Outro Lecture)

Difficulty level
Beginner
Speaker
Duration
38.17

This lecture is part of the Neuromatch Academy (NMA), a massive, interactive online summer school held in 2020 that provided participants with experiences spanning from hands-on modeling experience to meta-science interpretation skills across just about everything that could reasonably be included in the label "computational neuroscience". 

 

This lecture summarizes the concepts introduced in Model Fitting I and adds two additional concepts: 1) MLE is a frequentist way of looking at the data and the model, with its own limitations. 2) Side-by-side comparisons of bootstrapping and cross-validation.

Topics covered in this lesson
  • MLE is a frequentist way of looking at the data and the model, with its own limitations
  • Side-by-side comparisons of bootstrapping and cross-validation
  • A research example for model fitting. Point out that two main use of the model: 1) parameter estimation and its subsequent interpretation and 2) model simulation: both for the current data set as well as for new predictions beyond the fitted dataset
  • General steps for model building in real research
  • Why linear models have convex error functions
  • Introduction to GLM
Prerequisites