Commit 4705c3c2 authored by Simon Ciranka's avatar Simon Ciranka

Merge branch 'master' of ssh://arc-git.mpib-berlin.mpg.de:22973/ciranka/Ba

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 the commit.
parents 5f7781ee 6f28d403
......@@ -4,19 +4,11 @@ Implementing Binomal Updating in Wouters way.
These code chunks and results are implementing the models in wouters
mail form the 11.9.2018. Other than before, we now caluculate the log
likelihood of our participants estimates by assuming that the range of
possible estimates is normally distributed. The probability density
possible estimates is beta distributed. The probability density
under the specific probability estimate of the participant is now the
likelihood. For this i need a function that returns discrete values for
likelihood that we validate our parameter estimates with. For this i first need a function that returns discrete values for
the probability denisty in question.
I implemented the function you sent to me in your mail. At some point
when you talk about the arguments that you pass to the cumulative
densitiy function which you use to get a log likelihood; you say sigma
is sigma. In the example you sent to me; you do not use sigma as the
variance estimate of the beta updating before. You pass sigma with the
function one higher level that you then minimize. Is sigma as you use it
a free parameter of the model as well? So you dont have one but two free
parameters?
discretenormalMarble005(posResp,model_weight, sigma); % THIS IS THE CUSTOM FUNCTION WE BUILT FOR THIS STEP SEE BELOW (posResp is list made above. I guess of all possible resonses a subject can make).. sigma is sigma..
......@@ -51,7 +43,7 @@ Sequential Updating
In this model each *draw from the distribution* is weighted sequentially
in the beta update. Each trial there are 5 draws of the outcome
distribution that vary in their number of sucesses, k. At the beginning
of each trial - draws from the marbles a uniform prior with Beta shape
of each trial - draws from the marbles - a uniform prior with Beta shape
parameters *α* = 1 and *β* = 1 is assumed.
*p*(*x*|*n*, *k*) ∼ *B**e**t**a*(*α* = 1, *β* = 1)
......@@ -217,7 +209,7 @@ Data Loading
In this Chunk of Code i load the Data which i made with first loading
the rawdata in matblab, and squeezing the struct into two dimensional
data and run the script [01\_makeDataFrame.R](01_makeDataFrame.R)
data and run the script [01\_makeDataFrame.R](https://arc-git.mpib-berlin.mpg.de/ciranka/BayesianLearning/blob/master/Code/RoberstData/Entscheidung2behav/Make_R_DF.m)
```r
......
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