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Dec
8
awarded  Caucus
Dec
7
awarded  Nice Question
Oct
31
comment Computing Gradient and Hessian of a vector function
@SeeBees: Take a look at Pearlmutter and Schraudolph's work on the R technique that approximates exactly that in linear time.
Oct
16
awarded  Civic Duty
Oct
16
asked Does the diagonal of a gradient exist?
Oct
2
revised Finding $E(X \mid X > Y )$ when $X, Y \sim N(0,1)$
added 7 characters in body
Oct
2
revised Reordering matrix multiplication around diag operator
added 72 characters in body
Oct
2
revised Reordering matrix multiplication around diag operator
added 472 characters in body
Oct
2
answered Finding $E(X \mid X > Y )$ when $X, Y \sim N(0,1)$
Oct
2
revised Reordering matrix multiplication around diag operator
deleted 18 characters in body
Oct
2
asked Reordering matrix multiplication around diag operator
Sep
3
comment Intuitive explanation of a definition of the Fisher information
Is there a derivative missing in your definition of the score?
Aug
30
revised Determinant of Fisher information
added 16 characters in body
Aug
30
revised Determinant of Fisher information
added 16 characters in body
Aug
30
answered Determinant of Fisher information
Jul
2
awarded  Curious
Apr
10
comment Beta function derivation
@EricAuld: Bayes rule is much easier to think about without the denominator, which is just a constant given your observations. So: $P(q\mid a) \propto P(a\mid q) P(q)$. (These terms are called posterior, likelihood, and prior respectively.) Then, you can fill it in: $\int_0^x f(q \mid a) dq \propto \int_0^x f(q)f(a \mid q)dq \implies f(q \mid a) \propto f(q)f(a \mid q)$. And, the likelihood $f(a \mid q)$ is just $q$ for heads and $1-q$ for tails. Feel free to ask a question on stats.stackexchange. There are a lot of very helpful people there.
Apr
9
comment Beta function derivation
@EricAuld: Suppose I give you a coin whose bias $p$ you don't know. You flip the coin $n$ times and get heads $a$ times. Then the likelihood induced on $p$ is a Beta distribution. You can work out the Beta distribution up to normalization by writing out the product of the Bernoulli mass function $p^x(1-p)^{1-x}$ for each realization $x_i \in \{0,1\}$ (where $\sum_i x_i = a$ and $\sum_i 1-x_i = n-a$).
Apr
2
awarded  Tumbleweed
Mar
26
asked “Tessellate” $e^{-x}$