KL_mods: Compute KL divergence of one model from a second
In kleinschmidt/phondisttools: Tools for Analyzing Phonetic Cue Distributions
Description
Usage
Arguments
Value
Description
The KL divergence of mod2 from mod1 is the cost (in bits) of encoding data
drawn from the distribution of the true model (mod1) using a code that
is optimized for another model's distribution (mod2). That is, it
measures how much it hurts to think that data is coming from mod2 when
it's actually generated from mod1.
Usage
1
KL_mods(mod1, mod2)
Arguments
mod1
true model (list with fields mu and Sigma)
mod2
other model
Value
KL divergence of mod2 (candidate) from mod1 (true model), in bits.
kleinschmidt/phondisttools documentation built on May 20, 2019, 5:57 p.m.
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Description Usage Arguments Value
Description
The KL divergence of mod2 from mod1 is the cost (in bits) of encoding data
drawn from the distribution of the true model (mod1) using a code that
is optimized for another model's distribution (mod2). That is, it
measures how much it hurts to think that data is coming from mod2 when
it's actually generated from mod1.
Usage
1 | KL_mods(mod1, mod2)
|
Arguments
mod1 |
true model (list with fields mu and Sigma) |
mod2 |
other model |
Value
KL divergence of mod2 (candidate) from mod1 (true model), in bits.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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