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bio website suitdummy.blogspot.com
location Cambridge, MA
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visits member for 2 years, 6 months
seen Sep 17 at 21:23

I once launched swi-prolog and asked it a question:

ely:~/home$ prolog
Welcome to SWI-Prolog (Multi-threaded, 64 bits, Version 5.10.1)
Copyright (c) 1990-2010 University of Amsterdam, VU Amsterdam
SWI-Prolog comes with ABSOLUTELY NO WARRANTY. This is free software,
and you are welcome to redistribute it under certain conditions.
Please visit http://www.swi-prolog.org for details.

For help, use ?- help(Topic). or ?- apropos(Word).

?- love(math) is unrequited.
true.

Apr
6
comment Finding a force function from bodies in equilibrium
For the numerical approximation part, this sounds like a problem that will benefit from simulated annealing.
Apr
6
comment What matrices preserve the $L_1$ norm for positive, unit norm vectors?
Another way to put it is that the columns of $P$ are discrete distributions. Multiplying such a matrix by another distribution will yield a mixture of the columns, where the mixture weights sum to 1, hence that mixture must also be a discrete distribution.
Apr
5
answered Probability of absorption in a discrete Markov chain
Apr
5
comment Find the Bayes estimate of $θ$
That's almost correct, but what would happen if $\theta$ was smaller than 5? Could you ever draw a 5 from such a uniform distribution? Also, instead of just looking for the "peak" as I wrote, it sounds like you need the mean squared error. First work on getting to the point where you are confidence that you understand $p(\theta|5)$ then we can use the definition of mean squared error to finish off the problem.
Apr
5
comment Find the Bayes estimate of $θ$
What have you tried? Here is a hint: what is the probability $P(5|\theta)$ since you know that the probability of an observation is uniform between $0$ and $\theta$? Once you know this, then you can write down $P(\theta | 5)$ using Bayes' rule and find where it has a peak.
Apr
4
revised What is a “vanishing moment”?
added 259 characters in body
Apr
4
answered What is a “vanishing moment”?
Apr
3
comment Using Homotopy to solve system of nonlinear equations
Just as an aside, there's a whole theory of convex optimization to consider. You can use interior point methods, semi-Newton methods, gradient methods, or even basic line search methods to find roots. I'm not suggesting that your interest in homotopy methods is bad, but you'll probably be much better served by considering the half-century old ideas on root-finding with linear algebra before going off to homotopy.
Apr
3
revised Searching for numerical algorithm realization
added 589 characters in body
Apr
3
revised Searching for numerical algorithm realization
added 589 characters in body
Apr
3
revised Searching for numerical algorithm realization
added 589 characters in body
Apr
3
revised Searching for numerical algorithm realization
added 589 characters in body
Apr
3
comment Show/Prove two conditions when $P$ is an $n \times n$ matrix such that $P^2 = P$ and $P^t = P$.
Hint for the hint: What is $P(x-Px)$?
Apr
2
answered Searching for numerical algorithm realization
Apr
2
comment Making bounded an unbounded integral
Alternatively, it might be possible to pick any strictly-increasing, concave function $g(x)$ that is integrable w.r.t. $p(x)$, and then compose it with $F^{-1}(x)$ to get $f(x) = g(F^{-1}(F(x))) = g(x)$. I'm not sure under what conditions $g(F^{-1}(y))$ will also be strictly-increasing and concave though.
Apr
2
comment How can we bound $P\{X \ge t\}$ from below?
@J.D. Chebyshev's inequality is still an upper bound on probability (for a non-negative variable). It is where Markov's inequality is derived from, so it's likely that the poster already knew of this bound regardless. I suppose one can expand the absolute value usually used in it into two events, then use complements and multiply by -1 to get a statement of lower bounded probability, but it won't be in terms of a useful bound as it will always involve the other probability of the other "half-event" that you split out of the absolute value.
Mar
30
comment How can I calculate the CDF of this random variable?
When I do the computation by hand, I am able to easily perform the integral over the variable $x_{2}$, and then the resulting integral over $x_{3}$ is of the form $\int_{0}^{\infty}\frac{\alpha}{\epsilon + \beta x_{3}}\cdot{}\exp{(\frac{-x_{3}}{\Omega_{3}})}$, for constants $\alpha$, $\epsilon$, and $\beta$. According to Wolfram integrator, an integral of this type requires the Exponential Integral function Ei() to express the solution.
Mar
27
awarded  Tumbleweed
Mar
26
comment An example of a “pathological” power-spectral density function?
I don't like the 'avoid extended discussions in comments' rule, so I abstain from participating in chat-migrated things. I think it's most helpful if everything appears right here on the same page. But I will think much more about this and I'll post any follow-up result as an answer to avoid making the comments thread too long.
Mar
26
comment An example of a “pathological” power-spectral density function?
To clarify, you can construct functions with properties like $x^{2}\sin(1/x^{2})$ that have the symmetry and derivative properties you wanted. But the fact that PSD functions come specifically as Fourier transforms restricts what kinds of functions they can be. So it's a harder analysis problem to prove that you could't concoct a weird trig function with a singularity in its argument such that the singularity blows up for higher powers.