# 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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### Gradient of a multivariate function numpy

I'm trying to calculate the gradient of multivariate function g using NumPy. g = lambda w: -np.sin(np.pi*np.sum(w**2)) + np.log(np.sum(w**2)) ...
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### Neural Networks: How to get the gradient vector for the xOr problem?

I'm reading about neural networks, but the material I find is sometimes very abstract or just copies of something. Well, when considering the $xOr$ problem, I have a network in the following structure ...
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### How to find mean of multivariate normal distribution when holding a variable constant? [duplicate]

I wanted to know if there is a way to calculate the mean of a multivariate normal distribution when a certain variable is held constant. For example, if I had a continuous bivariate normal ...
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### Approximate / Standardize value in certain range

I have table with numeric values like ...
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### Backpropogation Derivatives

I've been working on trying to understand the backpropogation algorithm and the calculus behind it, and in my work I have stumbled across a sort of odd situation. I am just practicing on a 1 input, 1 ...
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### What is the mean average of $y=kg^t$ from $t=a$ to $t=b$ [closed]

Mean average of $y$ in $y=kg^t$ from $t=a$ to $t=b$. $g$ is a constant, $t$ varies. I have looked this up in textbooks and online and all I can find is the mean average of a function where $t$ is a ...
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### Is there a smart algorithm of finding the maximum of $X^{\top}a$ with $X$ and $a$ both belong to some compact convex set? [closed]

Suppose $X\in\mathcal{X}\subset R^k$ and $a\in\mathcal{A}\subset R^k$, where $\mathcal{X}$ and $\mathcal{A}$ are both compact convex set. Is there a systematic way of finding the maximum of $X^{\top}a$...
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### Book recommendations needed - building foundational knowledge for ISL - Introduction to Statistical Learning (by Gareth James)

I'm trying to build a data science base from scratch. I started a book called Introduction to Statistical Learning by Gareth James and found that there are many mathematical & statistical concepts ...
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### Differentiating $(y-X\beta)^T(y - X \beta)$ with respect to $\beta$

How do I differentiate $$(y-X\beta)^T(y - X \beta)$$ with respect to $\beta$. The result I saw was $$X^T(y - X\beta)$$
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### Backpropagation through time for stacked RNNs

I was able to find the partial derivative of the cost function with respects to a single variable without much difficulty. However, this requires propagating backwards through the network for each ...
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### Evaluation of Limit involved in the proof of Asymptotic Unbiasedness

We know that $S^{2}$ is an unbiased estimator of $\sigma^{2}$ and $S$ is a biased estimator of $\sigma$. But if $n\rightarrow\infty$, then $S$ is an asymptotically unbiased estimator of $\sigma$. I ...
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### Derivation of M step for Gaussian mixture model

Summary So to summarize my question, how can I take \begin{align} = \sum_{i=1}^{n}W_{i1} \left(log (1-\sum_{j=2}^{K}\pi_j) -\frac{1}{2} log(|\Sigma_1|) -\frac{d}{2} log(2\pi) -\frac{1}{2}(x_i-\mu_1)^{...
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### 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 ...
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### 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: $$F_{U_i}(x)=\sum_{n=0}^\infty \frac {2(-1)^nR^{k\alpha+n\alpha}x^{k+n}}{\Gamma(k)n!\theta^{k+n}(k+n)(k\alpha+n\alpha+2)}$$ ...
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### How to approximate expectation and variance of an integral from a discrete Time series financial dataset?

I have discrete time series financial data, with time($u$), price($S$) and someVariable($q$) which looks something like this. ...