Module 11:
Conditional distributions, conditional moments and prediction

Module 11

What this module is for

Both; this should be one of EQ’s strongest modules. Interview patterns include random sums, mixtures, updating distributions and choosing a useful conditioning variable.

Lesson 11.1 · Core

Conditional PMFs, PDFs and truncation

Depth: Interview mastery

QT/QR relevance: Both; revising a full distribution after information

Lesson 11.2 · Core

Conditional expectation as a random variable

Depth: Interview mastery

QT/QR relevance: Both; prediction and hierarchical reasoning

Lesson 11.3 · Core · QR Extension

Conditional variance and total variance

Depth: Interview mastery; extension at researcher depth

QT/QR relevance: Both; decomposing uncertainty

Lesson 11.4 · Core

Mixtures and elementary Bayesian probability models

Depth: Working proficiency

QT/QR relevance: Especially QR; uncertainty about probabilities and rates

Lesson 11.5 · Core

Random sums and compound distributions

Depth: Interview mastery

QT/QR relevance: Both; aggregate outcomes with random activity

Lesson 11.6 · Research Depth

Conditional expectation as the best squared-error predictor

Depth: Advanced / quant researcher depth — Extension

QT/QR relevance: Especially QR; foundations of regression and forecasting

Assessment

Module 11 assessment

Open assessment · No submitted assessment yet