Skip to content
April 3, 2019 | 3 Comments

Reality-Based Probability & Statistics: Ending The Tyranny Of Parameters!

Here it is! The one, the only, the peer-reviewed (and therefore true) “Reality-Based Probability & Statistics: Solving the Evidential Crisis” (the link is to the pdf, which is 11 MB; there are many pictures).

This a large review paper, summing up the problems I see in statistics, with a guide of how escape from the void.

There is no question computer scientists are kicking statisticians’ asses. Hard. As we saw yesterday. The answer is anyway simple: “AI”, which is nothing but lists of if-then statements, at least divorces, or does not concern itself much with, parameters. Statistics believes these strange entities have life! All practice, frequentist or Bayes, revolves around them. We are in orbit around a fiction.

Enough already! Let’s turn our eyes toward Reality. Here’s how.

Section 2: NEVER USE HYPOTHESIS TESTS

These are refinements to “Everything Wrong With P-values Under One Roof“, here with a mind toward cause.

P-values are now officially dead. The sooner we stop talking about them and about Reality, the better.

Section 3: MODEL SELECTION USING PREDICTIVE STATISTICS

Do we need hypothesis tests? No. And we only need model selection sometimes. If we’re forced to pick between models—and since most models are free in the sense they are only bits of code, we don’t always have to pick—then we should pick with a Reality-based metric and nothing else. Sometimes models cost model, because observations cost money, and therefore we will need to select. We do this based on Reality, not parameters.

Regular readers will be familiar with the mechanics of predictive inference, probability leakage, and all that. So you can skim this section, but pay some attention to the example.

Section 4: Y CAUSE

This is it! This is the missing element. The lack of focus on cause.

Parameter estimates are often called “effect size”, though the causes thought to generate these effects are not well specified. Models are often written in causal-like form (to be described below), or cause is conceived by drawing figurative lines or “paths” between certain parameters.

Parameters are not causes, and causes don’t happen to parameters. Probability is not real. Thus cause cannot operate on it. Parameters aren’t real: same deal.

Cause is probability and statistics is mixed up, to say the least; right ideas mix with wrong and swap places. There is no consistency.

Cause is conditional. Three small words packed with meaning. All probability is conditional, too, and in the same way. Once this is understood, we have made a great leap, and we can see what is possible to know about cause and what is not.

Section 5: TRUST BUT VERIFY

“Scarcely any who use statistical models ever ask does the model work? Not works in the sense that data can be fit to it, but works in the sense that it can make useful predictions of reality of observations never before seen or used in any way. Does the model verify?”

Then some ways this can and must be done.

Section 6: THE FUTURE

“No more hypothesis testing. Models must be reported in their predictive form, where anybody (in theory) can check the results, even if they don’t have access to the original data. All models which have any claim to sincerity must be tested against reality, first in-sample, then out-of-sample. Reality must take precedence over theory.”

WHERE?

This is in the inaugural edition of the Asian Journal of Economics and Banking, which does not yet have a web site (it’s that new). Paper copies are available at all better libraries.

April 2, 2019 | 8 Comments

AI Is Kicking Statistics’s Ass

Here’s the headline: “AI can predict when someone will die with unsettling accuracy: This isn’t the first time experts have harnessed AI’s predictive power for healthcare.

Unsettling accuracy? Is accuracy unsettling? Has AI progressed so far that all you have to do is step on an AI scale and the AI computer spits out an unsettlingly accurate AI prediction of the AI end of your AI life? AI AI AI AI AI? AI!

I’ve said it many times, but the marketing firm hired by computer scientists has more than earned its money. Science fiction in its heyday had nothing on these guys. Neural nets! Why, those are universal approximators! Genetic algorithms! Genes in the machine. Machine learning! Deep learning! Like, that’s deep, man. Artificial intelligence! Better than the real thing!

What has statistics got? Statistically significant? No, that’s dead. Thank God. Uniformly most powerful test? Unbiased estimator? Auto-regressive? Dull isn’t in it. You won’t buy any headlines talking about mu-hat.

What’s the difference between statistics and AI? Besides the overblown hype, that is? One thing: a focus on the results. That’s the reason AI is landing every punch, and why statistics is reeling. Statistical models focus on fictional non-existent hidden unobservable parameters, whereas AI tries to get the model to make good predictions of reality.

Now AI is nothing but statistical modeling appended with a bunch of if-then statements. Only this, and nothing more. Computers do not know as we know; they do not grasp universals or understand cause. They don’t even know what inputs to ask for to predict the outputs of interest. We have to tell them. Just as we do in statistics.

The reason AI models beat statistical ones is because AI models are tuned to making good predictions, whereas statistical models are usually tuned to things like wee p-values or parameter estimates. Ladies and gentlemen, parameters are of no interest to man or beast. The focus on them has forced, in a way, a linearity culture, whereas if we can’t write down the model in pleasing parameterized form, we’re not interested. Besides, we need that form to do the limit math of statistics of estimators of these parameters so that we can get p-values, which do not mean what anybody thinks they do.

AI scoffs at parameters and says, how can I create a mathematical function, however complex, of these input measures so that skillful, but not over-fit, predictions of the output measure are good?

That, and its understanding, or its attempts at understanding, cause. We’ve discussed many times, and it’s still true, that you can’t get cause from a probability model. Cause is in the mind, not the data. We need to be part of the modeling process. And so on. AI, though it’s at the beginning of all this, tries to get this right. I’ll have a paper tomorrow on this. Stay tuned!

I say AI will never make it. Computers, being machines, aren’t intellects; they are not rational creatures like we are. Intellect is needed to extract universals from individual cases, and computers can never do that—unless we have first programmed them with the answer, of course.

That is to say, strong AI is not possible. Others disagree. To them I say, don’t wait up.

We can’t discount the over-blownness of the comparison. Reporters love AI, and nearly all cherish the brain-as-computer metaphor, so we’ll apt to see intellect where it is not. Plus hype sells. Who knew?

It’s not all hype, of course. AI is better, in general, at making predictions. But headlines like the one above are ridiculous.

When all the number crunching was done, the deep-learning algorithm delivered the most accurate predictions, correctly identifying 76 percent of subjects who died during the study period. By comparison, the random forest model correctly predicted about 64 percent of premature deaths, while the Cox model identified only about 44 percent.

These are not unsettling rates. The “deep learning” is AI, the “random forest” is “machine learning” (if you like, a technique invented by a statistician), and “Cox model” is regression, more or less. I didn’t look at the details of how the regression picked its variables, but if it’s anything like “stepwise”, the method was doomed.

We always have to be suspicious about the nature of the predictions, too. These should be on observations never before see or used in any way. They should not be part of a “validation set”, because everybody cheats and tweaks their models to do well on the validation set, which, as should be clear, turns the validation set into an extension of the training set.

April 1, 2019 | 14 Comments

It’s White Male Privilege Week!

It’s White Privilege Week! What are you doing to celebrate?

Twitter started off a day early by forcing the tag #MyWhitePrivilege to trend.

As the multitude of links in this thread from Everything Oppresses prove (and this one; regular readers will recognize most of them), there is no force as potent in the universe as White Men. All want to be like us and have what we have. Yet all fear us and hate us. Why? Because we control everything. We have the power, in every institution, in every hallway, in every situation. Things happen because we will it, and for no other reason.

How do we wield this majestic force? Not directly: not by contact. By our presence.

God Himself, the most powerful and privileged among All and Creator of All, must therefore be a white man. Just look at the pictures!

As part of the White Male Privilege Week festivities, those who are non-white should refrain from commenting. This includes honorary non-white men.

The reason for this proscription is obvious. White men have the power to deny the existence of non-white-males. By restraining comments from non-whites, we make this power actual.

This may seem like a bad thing. It’s the opposite! This is the best gift we can give to the benighted people unfortunate enough not to have been born white and male.

As this sage article well argues, a new culture has developed in the West. That of The Victim. Victimhood denotes top status, appeal, self-worth, meaning “in much the same way that being recognized for bravery did in honor cultures.” There is no person as privileged as The Victim—after granting white men unimpeachable all-time First Place, of course.

Everybody wants to be a Victim; everybody longs for Victimhood. Except white men, of course. We are Victim makers.

Consider: If there is nothing better than being a Victim, and if white men have the power to do all bad things, it is the job of the white man to provide this victimhood to oppressed non-white-men everywhere. It is our sacred duty.

And, fellow white men, I must tell you, in brutal honesty, that most of us have been failing in this duty.

Yes. There are not nearly enough true Victims. Where is our sense of noblesse oblige!?

What we have now is a mere shadow of Victimhood. We have sad fat pimply purple-haired ladies, scrawny soyboy sodomites of color, skank spoiled rich (but non-white) writers for major newspapers, latinx flared-nostril wild-eyed boyfriendless cat ladies, and many more running around claiming to have suffered microaggressions from white males.

White Men: microaggressions? Is that the best we can do? It takes one million microaggressions to equal one true aggression. Yet it only takes one true aggression to make a Victim-For-Life. We are White Men! Let us call upon the power that has been granted us and have pity on the lesser sex and races! Our subjects cry out for real aggressions. Not these namby-pamby fictional fantasies non-whites are forced to cook up in their understandable quest for noble Victimhood.

We’re letting our side down, men. Social justice is moving off campus and to every facet of life. Look for it at your employer soon, if it’s not already there. The need for new Victims will only increase. If we do not get on this right away, there will not be enough Victims to go around—especially considering the number of non-white men being allowed into the country. Why, it will soon only be a handful of us white men left. And we’ll still be expected to make Victims of everybody else.

The solution to this crisis is to make true Victims of the victim wannabes.

Since true Victimhood is the goal and desire of every right thinking person, the more folks we can put into the Victim camp using our awesome white male powers, the more they will recognize our true gifts. Thus the more they will thank us. The more they will love us.

Come, white men. Celebrate White Male Privilege Week in the way it was meant to be.

March 31, 2019 | No comments

Summary Against Modern Thought: Divine Providence Does Not Exclude Contingency

Previous post.

God exists. And so does secondary causes. And, next week, it is proved So does your freedom!

THAT DIVINE PROVIDENCE DOES NOT EXCLUDE CONTINGENCY FROM THINGS

1 Just as divine providence does not wholly exclude evil from things, so also it does not exclude contingency, or impose necessity on things.

2 It has already been shown that the operation of providence, whereby God works in things, does not exclude secondary causes, but, rather, is fulfilled by them, in so far as they act by God’s power. Now certain effects are called necessary or contingent in regard to proximate causes, but not in regard to remote causes. Indeed, the fact that a plant bears fruit is a fact contingent on a proximate cause, which is the germinative power which can be impeded and can fail, even though the remote cause, the sun, be a cause acting from necessity. So, since there are many things among proximate causes that may be defective, not all effects subject to providence will be necessary, but a good many are contingent.

3 Again, it pertains to divine providence that the grades of being which are possible be fulfilled, as is evident from what was said above. But being is divided into the contingent and the necessary, and this is an essential division of being. So, if divine providence excluded all contingency, not all grades of beings would be preserved.

4 Besides, the nearer certain things are to God, the more they participate in His likeness; and the farther they are away, the more defective are they in regard to His likeness. Now, those that are nearest to God are quite immobile; namely, the separate substances which most closely approach the likeness of God, Who is completely immutable. But the ones which are next to these, and which are moved immediately by those which always exist in the same way, retain a certain type of immobility by the fact that they are always moved in the same way, which is true of the celestial bodies.

It follows, then, that those things which come after them and are moved by them are far distant from the immutability of God, so that they are not always moved in the same way. And beauty is evident in this order. Now, every necessary thing, as such, always exists in the same way. It would be incompatible, then, with divine providence. to which the establishment and preservation of order in things belongs, if all things came about as a result of necessity.

5 Furthermore, that which is necessary is always. Now, no corruptible thing always exists. So, if divine providence required this, that all things be necessary, it would follow that nothing corruptible exists among things, and, consequently, nothing generable. Thus, the whole area of generable and corruptible things would be removed from reality. This detracts from the perfection of the universe.

Notes In short, death is part of life.

6 Moreover, in every motion there is some generation and corruption, for, in a thing that is moved, something begins and something ceases to be. So, if all generation and corruption were removed as a result of taking away the contingency of things, as we showed, the consequence would be that even motion would be taken away from things, and so would all movable things.

7 Besides, the weakening of the power of any substance, and the hindering of it by a contrary agent, are due to some change in it. So, if divine providence does not prevent motion from going on in things, neither will the weakening of their power be prevented, nor the blocking of their power by the resistance of another thing. Now, the result of the weakness in power, and the impeding of it, is that a thing in nature does not always work uniformly, but sometimes fails in regard to what is appropriate for it naturally; and so, natural effects do not occur by necessity. Therefore, it is not the function of divine providence to impose necessity on things ruled by it.

8 Furthermore, among things that are properly regulated by providence there should be none incapable of fulfillment. So, if it be manifest that some causes are contingent, because they can be prevented from producing their effects, it would evidently be against the character of providence for all things to happen out of necessity. Therefore, divine providence does not impose necessity on things by entirely excluding contingency from things.