eljojo + statistics   19

Understanding the beta distribution (using baseball statistics) – Variance Explained
In short, the beta distribution can be understood as representing a probability distribution of probabilities- that is, it represents all the possible values of a probability when we don’t know what that probability is.

One of the most interesting outputs of this formula is the expected value of the resulting beta distribution, which is basically your new estimate. Recall that the expected value of the beta distribution is αα+βαα+β. Thus, after 100 hits of 300 real at-bats, the expected value of the new beta distribution is 82+10082+100+219+200=.30382+10082+100+219+200=.303- notice that it is lower than the naive estimate of 100100+200=.333100100+200=.333, but higher than the estimate you started the season with (8181+219=.2708181+219=.270). You might notice that this formula is equivalent to adding a “head start” to the number of hits and non-hits of a player- you’re saying “start him off in the season with 81 hits and 219 non hits on his record”).

Thus, the beta distribution is best for representing a probabilistic distribution of probabilities- the case where we don’t know what a probability is in advance, but we have some reasonable guesses.
statistics  stats 
november 2016 by eljojo
OpenCPU - Producing and Reproducing Results
OpenCPU is a system for embedded scientific computing and reproducible research. The OpenCPU server provides a reliable and interoperable HTTP API for data analysis based on R. You can either use the public servers or host your own.

Seamless R and JavaScript Integration
The OpenCPU JavaScript client library provides the most seamless integration of R and JavaScript available today. Enjoy simple RPC and data I/O through standard Ajax techniques. Check for yourself by trying the jsfiddle examples.
r  api  http  statistics 
august 2014 by eljojo

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