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What is the KL divergence of a normal distribution?
The KL divergence between a normal distribution with a mean of 0 and a standard deviation of 2 and another distribution with a mean of 2 and a standard deviation of 2 is equal to 500.
How to estimate KL divergence with IID samples?
Some ideas from that paper, which is about estimation of KL divergence with iid samples from absolutely continuous distributions. I show their proposal for one-dimensional distributions, but they give a solution for vectors also (using nearest neighbor density estimation).
When to use the KL divergence in Gaussian mixture?
In other words, we can use the KL divergence to tell whether a poisson distribution or a normal distribution is a better at approximating the data. The KL divergence is also a key component of Gaussian Mixture Models and t-SNE. For distributions P and Q of a continuous random variable, the Kullback-Leibler divergence is computed as an integral.
How to calculate the KL divergence in TensorFlow?
Just like numpy, in tensorflow we need to allocate memory for variables. For the variable q, we use the equation for a normal distribution given mu and sigma, only we exclude the part before the exponent since we’re normalizing the result. Just like before, we define a function to compute the KL divergence that excludes probabilities equal to zero.
When is the K-L divergence between discrete and continuous distributions?
When f and g are discrete distributions, the K-L divergence is the sum of f (x)*log (f (x)/g (x)) over all x values for which f (x) > 0. When f and g are continuous distributions, the sum becomes an integral: The integral is over the support of f, and clearly g must be positive inside the support of f for the integral to be defined.
Which is an example of the K-L divergence?
Recall that the K-L divergence is a measure of the dissimilarity between two distributions. For example, the previous example indicates how well the gamma ( a =2) distribution approximates the exponential distribution.