# Random

## Kalman filters

• Kalman-and-Bayesian-Filters-in-Python — Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters, extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

• filterpy — Kalman filtering and optimal estimation library in Python. Kalman filter, Extended Kalman filter, Unscented Kalman filter, g-h, least squares, H Infinity, smoothers, and more. Has companion book 'Kalman and Bayesian Filters in Python'.

## Particle filters

Better than Kalman filters but more difficult to implement.

## Markov random field

Probability distribution over random variables.

### Conditional random fields

Always involve inputs and outputs.

A generalization of logistic regression to have structured outputs, like chains, trees, or grids.

## Detrimental point processes

Can be used to increase the level of diversity in the samples that we generate. Other options are importance sampling, etc.

Alex Kulesza and Ben Taskar (2012) Determinantal point processes for machine learning https://arxiv.org/abs/1207.6083