[HN Gopher] Jensen's Inequality as an Intuition Tool
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Jensen's Inequality as an Intuition Tool
Author : sebg
Score : 56 points
Date : 2024-09-13 08:21 UTC (2 days ago)
(HTM) web link (blog.moontower.ai)
(TXT) w3m dump (blog.moontower.ai)
| lupire wrote:
| Jensen's inequality says that the average value of a function is
| biased toward the value where the derivative is closer to 0.
| That's where moving away from a sample point has the least impact
| on the output.
|
| It applies in scenarios that are "convex", which means that the
| derivative is monotonically increasing or decreasing, so "closer
| to 0" is a consistent direction.
| blackeyeblitzar wrote:
| Isn't this just saying things seek a local minima (or maxima)?
| fn-mote wrote:
| I would have spent longer on this article but it did not define
| Jensen's inequality before I got frustrated.
|
| I skimmed several section looking for it. :(
| bilater wrote:
| I had the exact same reaction and was about to rant here but I
| looked up the wikipedia article and to be fair to the author
| there is no simple easy way to explain this. The article
| actually does a decent job. :)
| daveguy wrote:
| There is a problem with the organization. At least one of the
| explanatory paragraphs in "2" of the table of contents should
| come before "why I find it interesting".
|
| Definitely recommend reading a little of this first:
|
| https://en.m.wikipedia.org/wiki/Jensen%27s_inequality
|
| (Could just provide a link to it near the beginning of the
| article for reference.)
| kristopolous wrote:
| Although I usually rail against Wikipedia math articles being
| gleefully esoteric jargon describing concepts in the most
| aggressively arcane way possible as if it's a competitive
| sport, the convex function article is fine. People have
| clearly done work on it
|
| (People might say, well isn't it obvious what convex means.
| My response is no, it's dealer's choice)
| iamcreasy wrote:
| The article does it around half way through,
|
| ...the inequality the way I learned it2:
|
| E[f(x)] >= f(E[x])
|
| ...if f(x) is convex
| lordofmoria wrote:
| This was interesting, especially with the DCF example at the end
| - it's pertinent to business sell decisions (assuming your
| ownership structure allows you to make a decision) should I sell
| at an 8x multiple of revenue, or hold at an X% growth rate and Y%
| cash flow? What's my net after 10 years?
|
| The point of Jensen's inequality if I understand correctly is
| that you'd underestimate the value of holding using a basic
| estimate approach, because you'll underestimate the compounding
| cash flow from growth?
| pfortuny wrote:
| It depends on whether future returns increase. One tends to
| draw the optimistic version of Jensen's inequality (and in
| general, of convex curves), but it also applies to decreasing
| functions.
| vladimirralev wrote:
| Looks like a reasonable intuition guide with a couple of caveats.
| This intuition only works for gaussian or may be a few other
| distributions, not for a general p(x). Then the EV rows in the
| tables are not actual EV quantities, only the total is an EV,
| that can confuse somebody. Overall I think, the point could have
| been carried better with a few nice charts showing the p(x),
| f(p(x)) and the E.
| seanhunter wrote:
| > This intuition only works for gaussian or may be a few other
| distributions, not for a general p(x)
|
| I don't think that's true. It's that if X is a random variable
| and ph is a convex function, then ph(E[X]) <=
| E[ph(X)].
|
| It's not necessary for X to be Gaussian, only that ph is
| convex.
|
| An intuitive way of thinking about it is if ph is convex then
| it is cup-shaped. So if I sample two points from X and draw a
| line ph(x_1) to ph(x_2) then that line will clearly lie above
| the points in x that are between x_1 and x_2 in the cup right?
| Jensen's inequality just generalises that to say what if I take
| all the points from X, then the expectation of ph(X) is going
| to sit above ph(E[X]). Because E[X] is just going to sit
| somewhere in the middle of X so ph(E[X]) is going to be down in
| the middle of the cup so is going to be smaller than
| (ph(x_1)+ph(x_2)+...ph(x_n))/n, which is E[ph(X)].
| vladimirralev wrote:
| That's what I mean. The Jensen inequality applies to any
| distribution, but the intuition presented in the post is only
| good for simple distributions and all examples are
| gaussian/binomial-like. It would be difficult to raise the
| same points with something multimodal or arbitrary.
| seanhunter wrote:
| Aah yes
| vermarish wrote:
| This is really cool! I've seen Jensen's inequality used many
| times over in my stats/ML classes, but the traffic example here
| gave me an "aha" moment about how it manifests.
|
| I like the visualizations of the expected value against the
| individual probabilistic components as well, though I wish there
| were more non-uniform distributions visualized. Perhaps if we
| take the traffic example and tweak the distribution to be non-
| uniform, that might make for a cool interactive viz.
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