Approximating Distribution of a Data Set If I have a set of data point and I want to approximate the distribution of that data set. What methods can be employed to fit the data set with the best most distribution. Whether it be gamma, normal, log normal, exponential, etc. I am trying to find the best distribution and the parameters that optimizes the best fit. What methods are out there to do so?
Here is the data, I am trying to approximate. With a distribution. I generated the data by running a 3,466 binary simulations (1,0) and summing the number of 1's in each simulation. According to probability theory, the sum of the outcomes of a Bernoulli distribution is  a binomial. But for the sake of being ignorant, if I didn't know this was binomial, how could I build a function that approximates the data. My end goal is to build an excel function that draws on the inverse of the density function and spits out a random number from the distribution.
x   #occurance  P(x)
1636    1   0%
1646    2   0%
1656    2   0%
1666    6   1%
1676    13  2%
1686    20  2%
1696    44  5%
1706    61  7%
1716    79  10%
1726    115 14%
1736    120 14%
1746    97  12%
1756    88  11%
1766    81  10%
1776    48  6%
1786    31  4%
1796    13  2%
1806    7   1%
1816    3   0%
1826    0   0%

 A: Discover an underly distribution of data is a typical example of application of neural networks. If is no clear what kind of function you are searching for, a neural net can built a, in general, multivariate non-linear function through the weights and the activation function chosen on units of the net. After training, the net can give you the value of probability for unknown values of x. Training is given by minimization of some type of error (typically quadratic error).
If you have an idea of what kind of function to search, a genetic algorithm can minimize the error of data using the parameters of the function (for example a gamma function). Population have several chromosomes that contain one solution (set of parameters) for each chromosome. Measuring the quadratic error between probability in the data set and probability (output value) of chromosome of all x values in the dataset using the parameters given by the chromosome for the chosen distribution, you can improve the global score of the population (using a selection pressure, with a fixed population length) and reach an approximation (good if dataset is big enough in relation to number of parameters) for the parameters of the distribution you are searching for.
Neural nets are more flexible, because can approximate any function, and then avoid to choose a distribution before apply the method, as need in general for other methods as Maximum Likelihood Estimation. Anyway, a genetic algorithm, I think, can be projected to use several density functions without problems.
Are you consider the approximation to a polynomial, using for example the Levenberg-Marquardt algorithm? Cannot generalize as a neural net, but give good results inside the domain of data.
A: Maximum Likelihood Estimation should give you the parameter values, for the given data set. (of course you need to decide what distribution you're going to use before hand)
http://research.microsoft.com/en-us/um/people/minka/papers/minka-gamma.pdf
