Recent content by verdverm

my intuition is that it's a bit vector to show which variable the derivative is with respect to

hmm I was just looking at: wolfram alpha for d/dx \ f(x,y)^{g(x,y)} = f^{(1,0)}(x,y)g(x,y)f(x,y)^{g(x,y)-1} + g^{(1,0)}(x,y)f(x,y)^{g(x,y)}log(f(x,y) \\ d/dy \ f(x,y)^{g(x,y) } = f^{(0,1)}(x,y)g(x,y)f(x,y)^{g(x,y)-1} + g^{(0,1)}(x,y)f(x,y)^{g(x,y)}log(f(x,y) \\ d/dx \...

I am having trouble finding the rule for the (partial) derivative of an expression like y = f(X)^{g(X)} can anyone help?

okay, i think people are looking to far into this... the problem I am having is simply this: given (possibly joint) gaussian probability distributions pHMM 1 |339| theta: 1.4544 0.2695 lens: 26.8225 6.2101 2 |24| theta: 0.8524 0.1335 lens: 2.4693 0.5381 3 |72|...

Not contradictory given that it is an iterative algorithm... a Hidden Markov Model (HMM) has many states, each with: - initial probability ( to start an observation series ) - transition probabilities ( to move from one state to another state ) { Matrix } * output probability(ies) ( the...

For a detailed specific reference: research.microsoft.com/pubs/144983/mod480-wang.pdf ( specifically the calculation of b_i(L) from section 3.3 ) I'm a little unclear on how the Bayesian comes into play... perhaps because of the formula, perhaps because there are several clusters a...

I would like to know how to calculate the probability that a normal distribution generated a sample. More specifically, I am clustering lines so I have several assumed normal distributions. Each cluster has a mean and variance/StdDev. of both slope and length. Given a set of clusters...