# Questions tagged [calculus]

For statistical questions involving calculus. Please use also a more statistical tag. For purely mathemathical questions about the calculus, it is better to ask at math SE https://math.stackexchange.com/

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### Variance of infinite weighted sum of normal distributions [closed]

We define the random variable $X_i\sim\mathcal N(0,I)$ where $\mathcal N$ is the multivariate normal distribution with a mean of $0$ and a variance matrix of $I$ (the identity). We also define a ...
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### I am not able to understand how did the elementwise multiplication came into the picture of backpropagation in neural networks

I have understood the backpropagation algorithm along with the chain rule well enough that I can derive it on my own, but I don't understand where the elementwise multiplication came from and how does ...
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### Multicollinearity in quadratic (polynomial) regression function [duplicate]

Multicollinearity problem could arise when we add quadratic variable in regression like this: So, one of the possible solutions to eliminate the problem is to add centered variables: This was ...
1 vote
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### Directional derivative in regression (coefficients, after all, are partial derivatives)

The coefficients in a (let's stick with linear for now) regression are the partial derivatives. A regression equation is a function of several variables, so all of the multivariable calculus tricks ...
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### Correlation between normal and log-normal variables

(This is not a homework question.) Let $(X_1 \sim N(\mu_1,\sigma_1), X_2 \sim N(\mu_2, \sigma_2))$ be a bivariate normal random variable with the correlation between $X_1$ and $X_2$ given by $\rho$. ...
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### Maths for deeply understanding backpropgation

I have been trying to develop a deeper understanding of Neural Networks so I can understand the libraries such as tensorflow and others. I have had good success with pereceptron models, and have a ...
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### How does one compute a variational derivative?

The expected regression loss is given as:$$E[L]=\int\int \{y(\mathbf x)-t\}^2 p(\mathbf x,t)d\mathbf xdt$$ To minimise the expected loss,Euler Lagrange equation is used which goes like this in the ...
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### Expected Predicted Error (EPE) with L1 loss

In Element of Statistical learning it is saying on page 20, equation 2.18. That using the L1 norm instead of the usual L2 norm leads to an $f(X)$ optimising the EPE being the median instead of the ...
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The function is $f(\mathbf{x}) = e^{-\frac{1}{2}\mathbf{x^TAx}}$, where $\mathbf{A}$ is a square symmetric matrix, and $\mathbf{x}$ is an n-vector. What I found were: \begin{align*} \nabla f ... • 121 0 votes 0 answers 192 views ### What is the first order derivative of linear regression's cost function using matrix calculus? For linear regression's cost function J(b), where X is a n*m matrix, b is a m*1 vector and y is n*1 vector: First order derivative with respect to vector b (coefficients) is shown to be Using the ... • 303 1 vote 0 answers 148 views ### On the convergence of infinite sum of a hypergeometric function resulted from a nested sum I am interested in finding the CDF of the sum U=\sum_{i=0}^N U_i where:F_{U_i}(x)=\sum_{n=0}^\infty \frac {2(-1)^nR^{k\alpha+n\alpha}x^{k+n}}{\Gamma(k)n!\theta^{k+n}(k+n)(k\alpha+n\alpha+2)} ...
I have discrete time series financial data, with time($u$), price($S$) and someVariable($q$) which looks something like this. ...