New Perspectives on Statistical Distributions and Deep Learning
In this data science article, emphasis is on science, not just on data. State of the coque iphone 6 g star art material is presented in simple English, from multiple perspectives: applications, theoretical research asking more questions than it answers, scientific computing, machine learning, and algorithms. I attempt huawei y635 coque here to lay the foundations of a new statistical technology, hoping that it will plant the seeds for further research on a topic with a broad range of potential applications. It is based on mixture models. Mixtures have been studied and used in applications for a long time, and it is still a coque huawei p20 hakuna matata subject coque brillante huawei p10 of coque huawei p9 lite bleu silicone active research. Yet you will find here plenty of new material.
In a previous article (see here) coque 360 samsung galaxy a5 2016 I attempted to approximate a random variable representing huawei p30 coque rose real data, by a weighted sum of simple kernels such as uniformly and independently, identically distributed random variables. The purpose was to build Taylor like series approximations to more complex models (each term in the series being a random variable), tooptimize data binning to facilitate feature selection, and to improve visualizations of histogramsbuild simple density estimators,Why I’ve found very interesting properties about stable distributions during this research project, I could not come up with a solution to solve all these problems. The fact is that these weighed sums would usually converge (in distribution) to a normal distribution if the weights did not decay too fast a consequence of the central limit theorem. And even if coque huawei p smart 2019chat using uniform kernels (as opposed to Gaussian ones) with fast decaying weights, it would converge to an almost symmetrical, Gaussian like distribution. In short, very few real life data sets could be approximated by this type of model.
I also tried with independently but NOT identically distributed kernels, and again, failed to make any progress. By «not identically distributed kernels», I coque de chantier huawei p20 mean basic random variables from a same family, say with coque huawei y5 ii voiture a uniform or Gaussian distribution, but with parameters (mean and variance) that are coque huawei p8 lite 2017 tardis different for each term in the weighted sum. The reason being that sums of Gaussian’s, even with different parameters, are still Gaussian, and sums of Uniform’s end up being Gaussian too unless the weights decay fast enough. Details about why this is happening are provided in the last section.
Now, in this article, starting in the next section, I offer a full solution, using mixtures rather than sums. The possibilities are endless.
2. Approximations Using Mixture Models
The problem is specified as follows. You have an univariate random variable Ythat represents any of double coque huawei y6 2019 your quantitative features in your data set, and you want to approximate or decompose it using coque huawei p10 lite dure a mixture of nelementary independent random variables called kernels and denoted as coque huawei p8 lite 2017 je peux pas X(n, k) for k = 1, ., n, with decreasing probability weights p(n, k) that converge to zero. The approximation of Y based on the first n kernels, is denoted as Y(n). By approximation, we mean that the data generated empirical distribution of Y is well approximated by the known, theoretical distribution of Y(n) and that as n tends to infinity, both become identical (hopefully).
Moving forward, N coque huawei p8 lite 2017 militaire denotes your sample size, that is the number of observations; N can be be very coque samsung a5 2017 frida kahlo large, even infinite, but you want to keep n as small as possible. Generalizations to the multivariate case huawei p20 coque marbre is possible but not covered in this coque double face huawei p20 lite article. The theoretical coque marvel huawei y5 2019 version of this consists in approximating any known statistical distribution (not just empirical distributions derived from data sets) by a small mixture of elementary (also called atomic) kernels.
In statistical notation, we have:
We also want Y(n) to converge to Y, in distribution, as n tends to infinity. Typically, each kernel X(n, k) is characterized by two parameters: a(n, k) and b(n, k). In the case of Gaussian kernels, a(n, k) is the mean and b(n, k) is the variance; b(n, k) is set to 1. In the case of Uniform kernels with Y taking on positive values, a(n, k) is the lower bound of the support interval, while b(n, k) is the upper bound; in this case, since we want the support domains to form a partition of the set of positive real numbers (the set of potential observations), we use, for any fixed value of n,a(n, 1) = 0 andb(n, k) = a(n, k+1).
Finally, the various kernels should be re arranged (sorted) in such a way that X(n, 1) always has the highest weight attached to it, followed by X(n, 2), X(n, 3) and so on. The methodology can also be adapted to discrete observations and distributions, as we will discuss later in this article.
Algorithms to find the optimum parameters
The goal is to find optimum model parameters, for a specific n, to minimize the error E(n). And then try bigger and bigger values of n, until the error is small enough. This can be accomplished in various coque huawei y6 2017 voiture ways.
The coque huawei p30 lite france solution consists in computing the derivatives of E(n) with respect to all the model parameters, and then finding the roots (parameter values that make the derivatives vanish, see for instance this article.) For a specific value of n, you will have to solve a non linear system of m equations with m parameters. In the case of Gaussian kernels, m = 2n. For uniform kernels, m = 2n + 1 (n weights, ninterval lower bounds, plus upper bound for the rightmost interval.) No exact solution can be found, so you need to use an iterative algorithm. Potential modern techniques used to solve this kind of problem include:EM algorithm..