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Models of run velocity include a Gaussian Distribution, or the Maxwell Distribution which govern the velocities and energies of molecules. An exponential distribution may describe the memoryless characteristic of run duration. We will build up a '''[[IGEM:IMPERIAL/2008/Prototype/Drylab/Data_Analysis/Alt_Models|database of models]]''', for future model fitting.
Models of run velocity include a Gaussian Distribution, or the Maxwell Distribution which govern the velocities and energies of molecules. An exponential distribution may describe the memoryless characteristic of run duration. We will build up a '''[[IGEM:IMPERIAL/2008/Prototype/Drylab/Data_Analysis/Alt_Models|database of models]]''', for future model fitting.
===Mechanical Model===
We have created a simple mechanical model of motile bacteria, depicted in the figure below.
[[Image:Mechanical_Model.JPG|center]]


==Fitting Models to Data==
==Fitting Models to Data==

Revision as of 09:29, 23 September 2008

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<html><a href=http://openwetware.org/wiki/IGEM:IMPERIAL/2008/Prototype><img width=50px src=http://openwetware.org/images/f/f2/Imperial_2008_Logo.png></img</a></html> Home The Project B.subtilis Chassis Wet Lab Dry Lab Notebook

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Model Fitting

Alternative Models

Models of run velocity include a Gaussian Distribution, or the Maxwell Distribution which govern the velocities and energies of molecules. An exponential distribution may describe the memoryless characteristic of run duration. We will build up a database of models, for future model fitting.

Mechanical Model

We have created a simple mechanical model of motile bacteria, depicted in the figure below.

Fitting Models to Data

In this first level of inference, we apply Bayes' Theorem. We first assume a particular model, and go on to derive the parameters of our model which maximises the data obtained.

Assigning Preferences to Alternative Models

In this second level of inference, we use the evidence contributed by the data to compare fitted models. Using Occam's Razor, we are then able to deduce the best model which fits our data.


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