#import "../../lib/mod.typ": *
= Background <background>

// This section presents the relevant theory made use of for the thesis. First a technical introduction to the underlying theory is covered. In @s.b.gaussian-models and #numref(<s.b.probabilistic-inference>), Gaussian models and probabilistic inference are introduced, respectively. These two topics are the theoretical base for understanding factor graphs and the corresponding methods for inference and reasoning about them, as detailed in sections #numref(<s.b.factor-graphs>)-#numref(<s.b.gaussian-belief-propagation>). Lastly the central concepts of the #acr("ECS") computing architecture is introduced. Each subsection is accompanied with clear examples to help in comprehending the use of the theory.

This section outlines the theory relevant to the thesis. It begins with a technical introduction to the underlying concepts. Gaussian models and probabilistic inference are introduced in @s.b.gaussian-models and #numref(<s.b.probabilistic-inference>), respectively. These topics form the theoretical foundation for understanding factor graphs and their associated inference methods, detailed in sections #numref(<s.b.factor-graphs>) to #numref(<s.b.gaussian-belief-propagation>). Lastly, the central concepts of the #acr("ECS") computing architecture are presented in @s.b.ecs. Each subsection includes clear examples to aid comprehension.

// #include "related-works.typ"
#include "technical-introduction.typ"
#include "factor-graphs.typ"
#include "rrt.typ"
#include "ecs.typ"
