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holly__ Now there's a nice integral of squared-error loss in Game::Numeric, it can be used to predict random variables (see Game-Decision) 10:12
Uploaded today
Now I need to debug both and more or less add tests 10:13
There's just simple tests now in Numeric and Decision 10:14
So I have 3 standard loss functions
S/So/So now 10:15
S/So/So now 10:16
*barf* stupid irc client
Altreus holly__: I wanna read your stuff but there's a lot. What's the sort of ... entrypoint module in all of this? :D 10:17
holly__ Game::Stats is very basic statistics, then Game-Numeric is standalone numerical functions (integrals for decision theory) 10:18
Then Game::Bayes is bayesian statistics extended and Game::Decision is what you have to read last, decision theory based on the 3 other packages 10:19
There's some design faults as I forgot some things in Game::Bayes 10:20
Game-Stats contains discrete distribution functionality
So a Game::Stats::DistributionPopulation contains just a list of chances 10:21
This is basically needed for calculating estimates, variances etc.
This is called a distribution in statistics 10:22
Game-Bayes needs more work so you just have some brute MAP learning and that's it for now, all based on the disrtibutionpopulation of Game-Stats ^^ 10:23
Then Numeric speaks for itself, definite integrals for the bayesian analysis
Then decision theory is quite nice in Game::Decision as you have several loss and risk functions which can be used in "Game"'s 10:24
Decision theory is loss and risk functionality
So you get fractions/probabilities with rewards and so on to calculate potentials (with integrals) 10:25
These things are needed for adaptive systems
To be more informative : Game-Decision is the best to learn in the end as it has clear game functionality, don't mind the dependencies as they are simple 10:26
Now TBH it's just a main pattern class with math function all the time, for speed instead of OOP 10:27
These things are faster than non-core/core dispatches
Altreus I see I see 10:28
I forgot I asked a question in IRC but I appreciate that you answered thoroughly anyway :D 10:29
Means I can digest it when I can manage
holly__ Sure, take care
In Game::Decision is an algorithm for 3 rewards r1 << r3 << r2, << == preferred over 10:37
That's also usable in games then there's just loss functions 10:38
These 2 are the basics of the whole system
Risk functions will come later together with Bayesian analysis which is for e.g. inference
holly__ needs to finish his book chapter 10:39
(About non-informative priors now)
These things will need more time as I have to understand the CS features of the noninformative priors 10:43
I need Haar measures and Fischer info matrices
Which probably will bootstrap Game::Matrix etc. 10:44
To conclude, if you understand the functionality of Game-Decision you'll be ok, the 3 dependencies are to be comprehended but are more easy 10:46
And for games pretty simple to use as they are just statistics and small functionality 10:47
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holly__ I am going to stop working on my Game:: libraries for this week 11:38
I'll rejoin on sunday 11:40
Evrything should be online in 6-12 hours
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holly__ I'm going to stop working on my Game::X libraries, everything should be online in 6-12-24 hours. So everyone has it for the weekend. CUL 11:49
(for this week)
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