Recent content by h6ss

I believe Levenberg-Marquardt is more robust than quasi-Newton methods for computational nonlinear regression, and generally works better for ill-conditioned problems. Also, have you tried different methods for calculating the eigenvalues? Or even, for inverting your Hessian matrix? That 170,000...

Consider the random variable ##Z=Y_1+Y_2##. Therefore, you have: ## \begin{eqnarray*} \frac{\Gamma(y_1+y_2+\gamma)}{(y_1+y_2)! \ \Gamma(\gamma)} = \frac{\Gamma(z+\gamma)}{z! \ \Gamma(\gamma)} = \frac{1}{z!} \cdot \frac{1}{\Gamma(\gamma)} \cdot \Gamma(z+\gamma) \\ \end{eqnarray*}## Do you have...

Yes! A commonly used example in introductory Bayesian statistics is the rare disease example. If a disease is very rare, then your probability of really having the disease given a positive test result remains low. Suppose a disease that affects 0.1% of the population is diagnosed with 95%...

What's particular with ideal randomness is that it is perfectly independent of everything, in particular knowledge and time. If the first machine did randomly choose between two objects, then I would assume the second machine would rightfully predict the first machine's choice assuming it knows...

If you use R then I suggest you going with R-Studio, there's a LaTeX editor integrated in it (R Sweave) as long as you have the (free) TeX libraries installed on your computer. I use that because I *always* use R when I write my LaTeX, and my macbook is getting quite old so having both in the...

If you have access to the dataset, you can also use density estimation techniques like the kernel density estimation. You can find a very good explanation of this technique here: http://www.mglerner.com/blog/?p=28 I hope this helps !

Hey! First of all, I'm curious as to what led you to think that the measurements of the analytes experiment follow a kind of Gumbel distribution. Were you given prior information stating that a Gumbel distribution was to be expected or did you just assume it followed that distribution by...

What you "discovered" is that P&Q added to P|Q is equal to P+Q, right? Here's a short explanation of what's really happening. Using the bitwise operator "&" for the logical operator "AND" and the bitwise operator "|" for the logical operator "OR", evaluating the expression P&Q basically creates...

I understand what you mean. Mathematical induction is not the same as induction in the informal sense. A proof by mathematical induction ultimately reduces to mathematical axioms just as any other good rigorous proof. It is constructive in the sense that it’s a perfectly rigorous technique. But...

It does, and that's an issue that has been discussed a lot in the past years, especially in the departments of mathematics, physics and chemistry. The problem is that a lot of people start those university degrees simply because they can (the collegial requirements are fairly low; only a few...

They require a collegial orientation in either pure or health science, which gives basic classes of physics and/or chemistry, both Calculus 1 and 2 and a class of linear algebra, or a collegial orientation in social science with maths, which again gives only both Calculus and Linear Algebra...

Here in Quebec, none of the five major universities require Calculus 3 for a major in Statistics, or even in Mathematics.

Not really, but maybe you'll find more information about what you're looking for here: https://en.wikipedia.org/wiki/Residual_sum_of_squares That is correct, but again, be careful with how you interpret this information.

The standard deviation between the two sets makes no sense. No such thing exists. Where did you get this formula from? As I said earlier, you can't just use the difference of the values for both datasets and label it as the standard deviation "between" them. Each dataset has a standard deviation...

What do you mean by that? I don't understand what you're looking for. If you're looking to compare both sets, you usually shouldn't work with the difference of both data sets per value. What you want is maybe the difference between a function (for example the mean) of one and the same...