1,775 reputation
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location Ottawa, Canada
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visits member for 2 years, 9 months
seen Dec 12 at 19:12

Corey Yanofsky, Ph.D.

I'm a biostatistician with an interest in financial statistics located in Ottawa, Canada. Philosophically, I'm a Bayesian of the Cox-Jaynes school. In practice I'm a statistical ecumenist whose role models are Michael I. Jordan and Andrew Gelman.


Dec
6
awarded  Necromancer
Dec
2
comment Unrealistically high significance when marginalizing over large number of parameters
To get the noise-only model onto the stage, I guess what you'd have to do is make $N_\rm{obj}$ a model parameter and do inference on it. You'll need proper priors over the object-specific parameters; otherwise, you'll run into the Jeffreys-Lindley thingy.
Dec
2
comment Unrealistically high significance when marginalizing over large number of parameters
You can expect Bayes to give you the best possible answer under the assumptions you've built into the model. Unless the full model nests the noise-only model within it such that posterior mass can concentrate on the noise-only possibility, Bayes will hallucinate, just as you've told it to.
Oct
14
comment What is the decision-theoretic justification for Bayesian credible interval procedures?
I've seen procedures that construct intervals from loss functions with univariate action spaces, but these have always struck the doctrinaire Bayesian in me as ad hockeries. My doctrinaire Bayesian self thinks that if you're going to report an interval, then the action space is best abstracted as a set of intervals, period. (The engineer in me is less rigid.)
Oct
9
comment What is the decision-theoretic justification for Bayesian credible interval procedures?
@RasmusBååth, you're basically asking, "what are the necessary conditions on the loss function for quantile intervals to be the solution to the minimization of posterior expected loss?" My intuition, just from the way the math works in the forward direction, is that this is pretty much it. Haven't proven it, though.
Sep
30
awarded  Explainer
Sep
24
awarded  Autobiographer
Apr
25
awarded  Enlightened
Apr
25
awarded  Nice Answer
Mar
9
awarded  Yearling
Feb
22
comment On the tractability of posterior distributions
Chamberlain Foncha, if you have the covariance matrix, you just calculate a matrix square root (using the Cholesky decomposition, or if $N$ is too big for that to yield stable results, the eigendecomposition) and then use a matrix-affine transformation of a product of standard normal random variates. This will work until $N$ is so big that the eigendecomposition computation outstrips one's computing resources.
Jan
30
comment Uniform Random on $(-\infty,\infty)$
Here's a relevant draft article from the arXiv.
Jan
27
asked Monotone likelihood ratio property: check my proof; also, who proved it first?
Jan
25
awarded  Nice Answer
Jan
11
awarded  Nice Question
Sep
30
awarded  Scholar
Sep
30
accepted What is the decision-theoretic justification for Bayesian credible interval procedures?
Sep
28
comment How do programs like BUGS/JAGS automatically determine conditional distributions for Gibbs sampling?
@Glen If you provide an example that's given you difficulty, I'll do the inspection.
Sep
26
comment Bayesian analysis problem
Your data are non-negative ratios, so take the logarithm and model on $\mathbb{R}$. A non-parametric method will probably serve you well. You might need to mix in a point mass at 0% (on the original scale, corresponding to $-\infty$ on the log scale).
Sep
25
comment What is the difference between posterior and posterior predictive distribution?
That posterior predictive distribution graph needs new axis labels and a caption or something. I get the idea because I know what a posterior predictive distribution is, but someone who's just figuring it out could get seriously confused.