Latent Dirichlet Allocation $($LDA$)$

$[2]$ D$.$ M$.$ Blei, A$.$ Y$.$ Ng$,$ and M$.$ I. Jordan, "Latent Dirichlet

Allocation," in Journal of Machine Learning Research, vol. $3,$

pp$.993-1022,$ Jan. $2003$

Introduction

LDA is a type of probabilistic topic model that

identifies underlying topics in a collection of

documents $2.$

It helps in discovering the hidden thematic

structure in large archives of texts, enabling easier

document management and retrieval.

Utilizes Dirichlet distributions to mathematically

model topic distributions within documents and

word distributions within topics.

Review

ModellingUsing OLS the following model is estimated:

$)i$

where $PAREN{T}_i$ denotes the amount that the parents of the $i$ th student are judged able to contribute to college expenses and HSRANK $H_i$ denotes the $i$

th student's GPA rank in high school, measured as percentage points $($i$.$e$.$ between $0$ and $100).$ Suppose the rank order is divided by $100,$ i$.$e$.$

$HSRAN{K}_i^{**}=$HSRAN$\frac{K_i}{100},$

than the estimate coefficient of HSRANK HS ${?}_i^{**}$ equals

$87\mathrm{.}4$

cannot be determined based on the above shown information.

$0\mathrm{.}874$