NetLogo banner

 Home
 Download
 Help
 Resources
 Extensions
 FAQ
 References
 Contact Us
 Donate

 Models:
 Library
 Community
 Modeling Commons

 User Manuals:
 Web
 Printable
 Chinese
 Czech
 Japanese

  Donate

NetLogo User Community Models

(back to the NetLogo User Community Models)

Drift

by Travis Monk (Submitted: 10/30/2013)

[screen shot]

Download Drift
If clicking does not initiate a download, try right clicking or control clicking and choosing "Save" or "Download".(The run link is disabled because this model uses extensions.)

## WHAT IS IT?

This model illustrates how sequential Bayesian inference can be applied to models where the state of the world being measured is dynamic.

## HOW IT WORKS

The predator has a neurone that fires a spike on a time step with probability Pr(S|D), as outlined in the manuscript. Given this conditional probability, which is plotted at the bottom of the screen, and a prior distribution Pr(D), we can infer the prey location Pr(D|S) by Bayes' rule. By letting the posterior distribution Pr(D|S) become the prior on the next time step, we can perform Bayes' rule recursively in time.

However, the prey deterministically drifts closer to the predator after every time step. When the state of the world is dynamic, we need to calculate the posterior using a two-step recursion as outlined in the text. After a time step, the posterior shifts with the prey and is then updated given the spiking output of the neurone. The process then repeats.

## HOW TO USE IT

Just click 'setup' and 'go.'

## THINGS TO NOTICE

The notes that appear during the model's running time point out key observations.

(back to the NetLogo User Community Models)