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Advanced Applied Statistics

This class was created by Brainscape user Niko Dice. Visit their profile to learn more about the creator.

By: Niko Dice
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Decks in this class (10)

1. Introduction, Frequentist inference
What is descriptive statistics,
What is inductive statistics infe...,
Statistical inquiry is usually mo...
32  cards
2. Parametric models, Exponential families
What is a univariate parameter fa...,
Name some familiar univariate fam...,
A probability functions must hold...
23  cards
3. Bayesian inference, Empirical Bayes
Write bayes rule,
What does bayes rule allow for,
How do we prove bayes rule
16  cards
4. Generalised linear models, Maximum likelihood
Show the probability density like...,
What do we use a likelihood funct...,
Why do we have a log likelihood
23  cards
5. Feature Selection and Shrinkage Methods for Regression
What are some downsides of mle,
Write the formula for bias and va...,
What is expected estimation error
5  cards
6. Survival analysis, Expectation-Maximisation
What does f_i s_i h_i and s_j_i t...,
How would we do this in a continu...,
What is censored data
7  cards
8. Bootstrap confidence intervals
How do we define standard confide...,
Why is it wrong to say the true i...,
What is the neymans construction ...
8  cards
9. Cross-validation
What is the prediction problem,
How do we quantify prediction error,
What is the true error rate
6  cards
11. Objective Bayes inference and MCMC
What are the problems of bayesian...,
Recall the bayesian setting,
How to choose g
9  cards
12. Statistical Modelling and Optimal Estimation
What are the basics to the mdl ph...,
Why do we need principles for mod...,
What is mdl rooted in
6  cards

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Advanced Applied Statistics

  • Class purpose General learning

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