User eternalGoldenBraid

23 votes

1 answer

9k views

What is the intuition behind Slater's condition in optimization? (And other constraint qualifications.)

asked Dec 29, 2016 at 23:28

14 votes

1 answer

1k views

Integration of a $k$-form over chains

asked Apr 11, 2016 at 14:24

10 votes

3 answers

607 views

What are the "technical troubles" with using a metric space rather than a topological space when defining an abstract manifold? (As in Spivak)

asked Apr 8, 2016 at 17:05

9 votes

4 answers

695 views

Is it "bad form" to count the number of outcomes in an event to compute a probability?

asked Feb 11, 2018 at 0:59

8 votes

2 answers

2k views

For a convex optimization problem, if primal and dual optimal variables exist, does strong duality hold?

asked Nov 10, 2017 at 7:07

6 votes

0 answers

76 views

What applications did Laplace have in mind when he said (in 1812) that probability had become "the most important object of human knowledge"?

asked Aug 19, 2018 at 23:30

6 votes

1 answer

4k views

What's the best way to think about the covariance matrix?

asked Apr 30, 2015 at 8:30

6 votes

3 answers

1k views

Understanding a derivation of the SVD

asked Apr 11, 2016 at 14:51

6 votes

2 answers

1k views

Intuition for bases having the same orientation

asked Mar 23, 2017 at 7:43

5 votes

4 answers

884 views

What's the best way to think about the Hessian?

asked May 9, 2015 at 8:36

5 votes

1 answer

134 views

Intuitive understanding of conditional density $f_{X \mid Y}(x \mid y)$

asked Jun 2, 2018 at 23:58

5 votes

1 answer

455 views

Precisely defining the conditional distribution of $X$ given that $Y = y$

asked Jun 8, 2018 at 22:09

4 votes

2 answers

683 views

What are the most fundamental applications of probability theory outside pure math?

asked Feb 10, 2018 at 8:49

4 votes

2 answers

5k views

Why does $\sqrt{n} (\bar X - \mu)/S$ have approximately a $t$-distribution?

asked Feb 5, 2019 at 23:03

4 votes

1 answer

229 views

Vector space is to manifold as convex cone is to?

asked Jan 23, 2017 at 10:36

4 votes

1 answer

145 views

How would you discover the normal distribution?

asked Feb 20, 2017 at 13:40

3 votes

1 answer

674 views

What is the significance of Gaitsgory and Lurie's proof of Weil's conjecture for function fields?

asked May 11, 2015 at 8:46

3 votes

1 answer

189 views

Has the theoretical explanation of Compressed Sensing been simplified since 2005/2006?

asked Jun 14, 2016 at 21:56

3 votes

1 answer

179 views

What is the precise definition of a "probability distribution"?

asked Aug 9, 2016 at 8:53

3 votes

1 answer

242 views

Is there an analogue of the Fourier transform based on hyperbolic trig functions?

asked Dec 19, 2018 at 10:34

3 votes

1 answer

628 views

Can the spectral theorem from linear algebra be proved easily using the SVD?

asked Nov 14, 2017 at 20:04

2 votes

1 answer

257 views

Confidence interval for mean of non-normal distribution based on a small sample

asked Feb 10, 2019 at 21:56

2 votes

1 answer

131 views

Under what conditions is the solution to a Lasso problem the same as the solution to a corresponding $\ell_0$-penalized least squares problem?

asked Nov 4, 2019 at 7:01

2 votes

1 answer

100 views

Basic but illuminating examples of statistical modeling

asked Oct 21, 2018 at 21:31

2 votes

0 answers

53 views

Is there any reason not to use the notation $p_{X \mid Y = y}(x)$?

asked Nov 22, 2018 at 6:06

2 votes

1 answer

1k views

How do you derive the conjugate gradient method?

asked Feb 20, 2017 at 23:42

2 votes

1 answer

453 views

Parameterized families of functions, other than neural networks, that are useful for machine learning

asked May 29, 2017 at 20:52

1 vote

0 answers

187 views

Deriving the Kalman filter using basic facts about normally distributed random variables

asked May 30, 2016 at 10:24

1 vote

1 answer

233 views

SVD of matrix with positive determinant

asked Mar 24, 2017 at 10:26

1 vote

2 answers

274 views

An approach to learning math that does not emphasize problem solving

asked May 8, 2017 at 22:41

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36 Questions

What is the intuition behind Slater's condition in optimization? (And other constraint qualifications.)

Integration of a $k$-form over chains

What are the "technical troubles" with using a metric space rather than a topological space when defining an abstract manifold? (As in Spivak)

Is it "bad form" to count the number of outcomes in an event to compute a probability?

For a convex optimization problem, if primal and dual optimal variables exist, does strong duality hold?

What applications did Laplace have in mind when he said (in 1812) that probability had become "the most important object of human knowledge"?

What's the best way to think about the covariance matrix?

Understanding a derivation of the SVD

Intuition for bases having the same orientation

What's the best way to think about the Hessian?

Intuitive understanding of conditional density $f_{X \mid Y}(x \mid y)$

Precisely defining the conditional distribution of $X$ given that $Y = y$

What are the most fundamental applications of probability theory outside pure math?

Why does $\sqrt{n} (\bar X - \mu)/S$ have approximately a $t$-distribution?

Vector space is to manifold as convex cone is to?

How would you discover the normal distribution?

What is the significance of Gaitsgory and Lurie's proof of Weil's conjecture for function fields?

Has the theoretical explanation of Compressed Sensing been simplified since 2005/2006?

What is the precise definition of a "probability distribution"?

Is there an analogue of the Fourier transform based on hyperbolic trig functions?

Can the spectral theorem from linear algebra be proved easily using the SVD?

Confidence interval for mean of non-normal distribution based on a small sample

Under what conditions is the solution to a Lasso problem the same as the solution to a corresponding $\ell_0$-penalized least squares problem?

Basic but illuminating examples of statistical modeling

Is there any reason not to use the notation $p_{X \mid Y = y}(x)$?

How do you derive the conjugate gradient method?

Parameterized families of functions, other than neural networks, that are useful for machine learning

Deriving the Kalman filter using basic facts about normally distributed random variables

SVD of matrix with positive determinant

An approach to learning math that does not emphasize problem solving