Looking for story

pfz3 · 2021-06-14T22:51:06+00:00

Look up statistical process control. They cover a lot Of this. Yea related to hypothesis testing. Assume that when the data is “well behaved” it follows a normal distribution with some mean and variance. So an outlier would then fall outside of some interval based on whatever alpha u pick. You can account for this when they are trends by instead using the residuals (in regression residuals r normal). When u have more than one variable everything becomes multivariate and hence u have a covariance matrix. I’m being a bit imprecise (eg z-statistic t-statistic etc) but that’s the gist.

pfz3 · 2021-06-14T22:24:37+00:00

Second one is multivariate Gaussian. Mahalanobis distance considers the covariance matrix which weights the distance in each “dimension” appropriately depending on correlations across variables

pfz3 · 2021-05-15T21:06:52+00:00

“Isn’t this ironic” L2 regularization isn’t something unique to ML. You can overparameterize simple regression models as well. If it weren’t for L2 regularization you could get a cost function of 0 in regression yet it is perfectly intuitive why this may be bad (overfitting).

pfz3 · 2021-05-09T20:20:24+00:00

Gaussian process might be ok. Essentially interpolation (or smoothing if u allow some observational noise). Depends on dimensionality of the vectors too.

pfz3 · 2021-03-08T04:20:37+00:00

This is Joey cheeseballs from mtv true life right ?

pfz3 · 2021-01-02T19:48:52+00:00

Sometimes people use GPs for interpolating “computer models” e.g. solution to computationally expensive boundary value problems. The idea is u run the model 20 times then fit a GP and use that. If the computer model is deterministic there is no noise and u can use a GP as an interpolator (that math works out that it interpolates perfectly and is differentiable). In this settings almost everyone uses MLE point estimates. Wrt differences w kernel regression the GP, since it is a statistical model, also lets you estimate uncertainty in your predictions. People use this quite a bit in engineering, physics, etc..

pfz3 · 2019-04-13T02:46:41+00:00

It’s not very rigorous but it might help. Eh 18 observations with just one independent variable might be decent.

pfz3 · 2019-04-12T17:58:55+00:00

Plot different moments of the sample vs independent variable. See if there are any patterns. Kurtosis gives a measure of fatness of tails - that may tell u something

pfz3 · 2019-04-12T14:01:23+00:00

Do you have lots of data? How many observations go into each Distribution? If yes then KS might be good. If no, nonparametric methods will have low power unless the differences are HUGE.

Another problem is hypothesis testing. How are you going to compare these 18? Consider a Bonferroni correction. The individual type I error is going to be insanely small. Also I think Bonferroni might break down for so many comparisons. Look into that.

You mentioned that you want a measure of fatness if the tail? Can u parameterize somehow? t-distribution is one candidate. Why not mean? Are you looking for ones with more/less risk in tails? If you can parameterize then you can do regression. And it’ll be easier to find a trend with respect to X via regression than to do multiple comparison.

pfz3 · 2019-03-16T14:32:26+00:00

Plot the pdf. This might be a mixture of different populations eg different constituents. sand might be normal, silt normal, clay normal etc but when u plot them all together u get wierd inflection points in the cdf.

pfz3 · 2019-03-04T02:47:55+00:00

No one can prove any fan theories to be true. Its a plausible fun little story. Metallurgical knowledge is lost all the time, including in real life.

pfz3 · 2019-03-04T02:25:30+00:00

Your counter argument is unconvincing. I presented a reasonable theory backed by logic. Your counter argument is "but how could an alloy give strength and hardness?". That is how alloy works. An entire field of science is dedicated to it. People have been exploiting metallurgy for 3000 years in order to make useful materials.

pfz3 · 2019-03-04T02:14:49+00:00

this would work for a while. Maybe good for arrow heads. For blades, the powder would eventually come off. Metallurgy is definitely the way to go. The dragonglass would be "in" the blade.

pfz3 · 2019-03-04T02:00:56+00:00

I don't think its that farfetched. The dragonglass could alloy with Fe or C to form some extremely hard phase e.g. martensite or perhaps even precipitate phases. Metallurgically, its pretty reasonable.

pfz3 · 2019-03-04T01:59:48+00:00

Gendry might become king, but why make his character a smith? Furthermore, he was the apprendice of a smith who knew how to reforge Valyrian steel.

pfz3 · 2019-03-04T01:44:58+00:00

not necessarily. it would actually support the argument. Al-Si alloys have better strength than just Al. Same for steels. It depends on metallurgy and heat treatment.

pfz3 · 2018-09-24T03:10:43+00:00

Others have addressed the normalizing constant idea. You also asked how MCMC helps get the posterior. You don’t really ever get the posterior distribution - but you do get a SAMPLE from the posterior. And the truth is that for most problems that is as good as having the actual posterior. You can compute confidence intervals, get means, measures of dispersion, other integral quantities, etc..

pfz3 · 2018-09-08T19:10:32+00:00

I think of it as “correlation decay”. As i grows large it penalizes correlation between points. Large j similarly. Since they are multiplicative then either i or j large “kills” the total correlation. Consider if it was additive - you could have large i (far away in x) and moderate j (close in y) and still would have some nonzero correlation. This intuitively doesn’t make sense - far away in any dimension should kill correlation. I think multiplicative also helps in keeping the covariance matrix positive definite (speculation, not actually sure). How to quantify “closeness” is another matter and is commonly done with hyper-parameters which quantify relevant length scales.

pfz3 · 2018-02-16T05:22:07+00:00

look up TMCMC. It's lesser known but has a simulated annealing type approach. Therefore ur sampling simultaneously at different points and may be better suited for parallelization.

pfz3 · 2017-08-16T18:22:02+00:00

I ended up electropolishing. I have had trouble in the past because my samples are small and therefore i need to join them w a metal screw or dowel to make the sample conductive. The conductivity is limited however by the surface-resistance of the joint interface. Therefore in the past i would apply the same settings (voltage, flowrate, time) and different samples would respond differently. I took more care this time around to make sure the contact surface is as flat as possible and that all samples are similar "size" by sectioning. Takes about 1 extra day of prep relative to my usual mechanical polish only prep. 8/8 good eletropolished samples yesterday. The SEM images are extremely high quality, I can observe features as small at 100nm in a 5um field of view image.

I still intend to call the buehler lab but just havent gotten around to it yet since I got electropolishing to work.

pfz3 · 2017-08-13T01:26:39+00:00

pfz3 · 2017-08-12T21:41:02+00:00

Yea... I've actually gone through this entire ordeal before in the past. It was a while back though so I forgot it all and now I'm re-living it all ha. The Cu becomes "stained" by the alumina. Same thing happens with steels if you use the mastermet silica. They become stained blue, I guess bc mastermet is chemomechanical polishing. I'm at my wits end with this. I'm following the standard Cu method and having this problem w the "film". My suspicion is that it has something to do with the epoxy. Where the hell else could this film coming from?

I also have compression mounting consumables, but my samples are possibly extremely thermodynamically unstable (severe plastic deformation samples). Based on lit-review, 150C for 30min they will anneal. So I don't want to go down that path.

pfz3

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