Maximum likelihood and experimental data
- To: mathgroup at smc.vnet.net
- Subject: [mg114804] Maximum likelihood and experimental data
- From: max spoor <max.spoor73 at gmail.com>
- Date: Fri, 17 Dec 2010 03:31:22 -0500 (EST)
I am new to Mathematica and have a basic question about comparing the
fit of different models to
experimental data on actual frequencies of play in 2x2 games (e.g. I
compare predictions like [0.439; 0.604] to frequencies like [.455; .
Some of the models are non parametric, while others are, so I would
like to "punish" the latter for abundance of parameters, say using the
Akaike information criterion for likelihood estimation.
My question is what distribution should I assume about the underlying
data? Also, is there a requirement in terms of the minimum number of
Any suggestions or code example will help!
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