Skip to main content

Partially observable hidden Markov model

Project description

pohmm is an implementation of the partially observable hidden Markov model, a generalization of the hidden Markov model in which the underlying system state is partially observable through event metadata at each time step.

An application that motivates usage of such a model is keystroke biometrics where the user can be in either a passive or active hidden state at each time step, and the time between key presses depends on the hidden state. In addition, the hidden state depends on the key that was pressed; thus the keys are observed symbols that partially reveal the hidden state of the user.

For examples and documentation, see https://github.com/vmonaco/pohmm

Project details


Release history Release notifications

This version

0.5

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for pohmm, version 0.5
Filename, size File type Python version Upload date Hashes
Filename, size pohmm-0.5.tar.gz (143.8 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page