It is quite tempting, therefore, to use weighted sums of PDFs to construct new PDFs and in this post we shall see how we can use a simple probabilistic argument to do so. ]]>

Unfortunately for large numbers of dimension the calculation of the approximation will still be relatively expensive and will require a significant amount of memory to store and so in this post we shall take a look at an algorithm that only uses the vector of first partial derivatives. ]]>

Now that we've got the theoretical details out of the way it's time to get on with the implementation. ]]>

Before we take a look at them, however, we'll need a way to step toward minima in such directions, known as a line search, and in this post we shall see how we might reasonably do so. ]]>

This time we shall see how we can approximate the function that defines the relationship between them without actually revealing what it is. ]]>

This time we shall take a look at the binomial distribution which governs the number of successes out of

This time we shall take a look at the distribution of the number of failures before a given number of successes, which is a discrete version of the gamma distribution which defines the probabilities of how long we must wait for multiple exponentially distributed events to occur. ]]>

We have already seen that if waiting times for memoryless events with fixed average arrival rates are continuous then they must be exponentially distributed and in this post we shall be looking at the discrete analogue. ]]>

These govern continuous memoryless processes in which events can occur at any time but not those in which events can only occur at specified times, such as the roll of a die coming up six, known as Bernoulli processes. Observations of such processes are known as Bernoulli trials and their successes and failures are governed by the Bernoulli distribution, which we shall take a look at in this post. ]]>

This time we shall take a look at another family of special functions derived from the beta function B. ]]>

Whilst we didn't originally derive the Cauchy distribution in this way, there are others, known as ratio distributions, that are explicitly constructed in this manner and in this post we shall take a look at one of them. ]]>

An easy way to create rotationally symmetric functions, known as radial basis functions, is to apply univariate functions that are symmetric about zero to the distance between the interpolation's argument and their associated nodes. PDFs are a rich source of such functions and, in fact, the second bell shaped curve that we considered is related to that of the Cauchy distribution, which has some rather interesting properties. ]]>