Introduction to Bayesian Filter

Some better grounding might help to start with, and - unusually - Wikipedia gives a pretty solid and tangible context to frame this in (With greater purpose and clarity than the article IMO):

> A Bayes filter is an algorithm used in computer science for calculating the probabilities of multiple beliefs to allow a robot to infer its position and orientation. Essentially, Bayes filters allow robots to continuously update their most likely position within a coordinate system, based on the most recently acquired sensor data. [...] In a simple example, a robot moving throughout a grid may have several different sensors that provide it with information about its surroundings. The robot may start out with certainty that it is at position (0,0). However, as it moves farther and farther from its original position, the robot has continuously less certainty about its position; using a Bayes filter, a probability can be assigned to the robot's belief about its current position

https://en.wikipedia.org/wiki/Recursive_Bayesian_estimation#...

here you go

https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...

Yeah this article isn't that clear so don't worry about it.

For example, to suppose that particle filtering is an extension of Bayesian probability is a category error. If this just stemmed from communication difficulties, it still matters from a reader perspective.

My rule of thumb for selecting technical books on the library is: if the title says "basic", "fundamental" or "introduction" then it's in depth and there are pre-requisits; if the title says "advanced" then it's content-free bullshit.