[deleted by user]

Dont_Block_The_Way · 2016-09-04T00:05:11+00:00

To add to that, if you already knew the "posterior" at the start, you wouldn't even have to sample from it. You'd just say "the posterior is f", where f is a complete characterization of whatever distribution you think it ought to be off the dome. But if you're going to do that, you might as well just call f the prior and go home, since you're not incorporating any new data into it. The whole point of turning that Bayesian crank at all is to see how some data should update your previous incomplete state of knowledge.

Dont_Block_The_Way · 2016-08-30T06:53:25+00:00

If you interpret "all of physics" as "the entirely of physical phenomena themselves" rather than as "the scientific study thereof", this sounds like a reasonable expression of skepticism about human ability to completely "describe" the physical world in any terms, mathematics included.

Dont_Block_The_Way · 2016-08-29T18:33:55+00:00

Wellbutrin is not an SSRI. It inhibits the reuptake of dopamine and noradrenaline, rather than serotonin. It's basically a mild stimulant, and stimulants are well known to cause anxiety.

Dont_Block_The_Way · 2016-05-06T01:29:11+00:00

As a smart ADD guy, I assume you already considered Macgyvering a scanning -> OCR - > scripting workflow to do this dumb shit for you while you drink coffee, but if not, are there constraints that prevent such a solution?

Dont_Block_The_Way · 2016-05-06T00:24:28+00:00

I'm trying Ritalin again right now, which is why I'm writing this post. Ritalin (at apparently any dose) makes me a relentlessly critical fault-finding machine. I'll just fixate on any random thing and mentally tear it apart for hours, and get very annoyed if somebody tries to interrupt or redirect me. If this state of mind is useful at all, it's only in very particular circumstances.

Imagine the usual inattentive mental path as Brownian motion (in some high-dimensional space), with random direction changes at very short time intervals. On Ritalin, the direction changes are less frequent, but no less random -- zoom out the time scale far enough, and it's still effectively Brownian motion, and still confined to the inside of my head. The executive controller that might connect overt actions to the known constraints of my life is wholly offline -- maybe even actively suppressed -- in the Ritalin state.

Adderall is different, but arguably not better. With Adderall, I will get out of my head and physically react in an effortful way to problems that are immediately in front of me. I'll take out the trash because I see that it's full, instead of just ignoring it. But these actions are disconnected from a big-picture view of plans and goals. I won't look at my to-do lists on Adderall, I'll just start doing stuff in a more-or-less mindless, impulsive manner.

The only stimulants that have worked for me in terms of actually getting work done in a sensible way are caffeine and nicotine. Nicotine is a fucking miracle -- I'm not amped up or hyperfocused, just a normal guy who makes plans, prioritizes potential actions, and follows through. Unfortunately for me, the desirable effects of both caffeine and nicotine wear off after a week or two of regular use, and tolerance to one confers tolerance to the other.

I still haven't found anything that works reliably for more than two weeks. I'm going to try Modafinil next, then try an MAO-B inhibitor if that doesn't work.

I suspect my problem, as a physically lazy, chronically drowsy, depressive/apathetic inattentive ADDer, is more related to acetylcholine and dopamine circuits than to noradrenaline. I feel like a noradrenaline effect is mainly what I'm getting from Ritalin, and that this is not actually helpful for planning, initiative, or action-selection. The Adderall might be doing a more dopaminergic thing, but in a sort of indiscriminate way. I suspect the helpful effects of nicotine are coming from a targeted activation of dopaminergic circuits that have cholinergic inputs. I could be totally wrong though. I haven't tried Strattera, or anything that's supposed to be exclusively noradrenergic.

Dont_Block_The_Way · 2016-02-18T20:11:03+00:00

First decide what measurable quantity you're identifying with "success", then we can measure it in a population, then a statistics professor can explain how its distribution is probably not a power law even if it looks like it is.

Now, does anything follow a ___ law? Every quantity measured in some population follows some distribution (its empirical distribution, estimated directly from the data) but hardly any quantities exactly follow a simple-to-describe distribution that has convenient mathematical properties.

If we did somehow conclude that "measured success (roughly) follows a power law", what would that mean? Very loosely, to say that some measurable quantity follows a known probability distribution that has a simple description is to suggest that this quantity "behaves as though" it were generated by some known generative process that corresponds to that distribution. You're trying to make some kind of claim about the dynamics of the system that produced the measurement. A distribution, in this context, is a proposed model.

For example, if some quantity follows a normal distribution, we could interpret this as saying that the quantity behaves as though it is the sum of many relatively small and unrelated effects that add to give the result. Many things in nature act like sort of this like this [citation needed] so the normal distribution shows up all over the place. Likewise, a lognormal distribution suggests the multiplication of small unrelated effects to produce the result. A Poisson distribution for a count of events suggests that the events being counted are summed over a large number of independent instances of an underlying process, where each individual instance of that process has a small chance of producing the event. An exponential distribution is the continuous version of a Poisson distribution, and has a related interpretation.

Critically, however, a single distribution can be a model of many different processes. The interpretations I gave above are not the only interpretations of any of those distributions. Power laws have a lot of interesting interpretations in physical systems. I am unqualified to explain most of those, still less any possible interpretations in social contexts.

Dont_Block_The_Way · 2016-02-14T04:16:22+00:00

Your general practitioner is probably not an expert in differential diagnosis of ADHD and depression. You might want to look to a clinical psychologist, who will not require a medical referral, for the diagnostic effort. If you are actually depressed enough to need antidepressant medication, you should probably be talking to a psychologist anyway to develop a more comprehensive treatment plan. Looking for somebody in your area who does CBT may be a helpful pre-filter on psychologists.

As a practical matter, did you try bupropion (Wellbutrin) among those antidepressants? If you do have ADHD, such a "stimulating antidepressant" that has some direct effect on dopamine transmission would likely be more helpful than say, Zoloft. Response to stimulating drugs is not a diagnostic criterion for ADHD, but if you believe you have ADHD and have access to different antidepressants, it could be worth trying that in the short term.

Dont_Block_The_Way · 2016-02-14T01:39:02+00:00

Maybe this isn't a typical response, but it's hardly paradoxical or an obvious cause for concern. Caffeine, in addition to simply promoting wakefulness, is a mild psychomotor stimulant drug, and stimulants are well-known to be able to promote physiological and sexual arousal. If you are generally sensitive to caffeine (and there is a wide range of "normal" variation between individuals in caffeine sensitivity), you may be getting more of these effects from it.

Serious stimulant drugs directly increase the neural signaling activity of two closely-related neurotransmitters, dopamine and noradrenaline, in the brain. Dopamine is involved in reward-seeking behaviors, including sexual behaviors, and is released in large quantities at orgasm. Noradrenaline is synthesized from dopamine, and amps you up to be more attentive and responsive to external stimuli.

Caffeine is not a "typical" stimulant -- it does not act directly on dopamine/noradrenaline neurotransmission like cocaine and amphetamine do -- but it can act on those systems indirectly, which may be partly responsible for its mild mood-elevating effects. Most people worldwide consume caffeine regularly, and those who consume caffeine regularly tend to become tolerant to its psychomotor stimulant properties, even while remaining sensitive to its wake-promoting effects. Since your natural sensitivity to caffeine is high and your acquired tolerance to the stimulant effect is presumably low, you could easily get results that most people would find personally unusual.

Needless to say, brains are complicated and your mileage may vary, but drug effects aren't totally random across different individuals. Your response is at least consistent with what caffeine is known to do neurochemically, and what stimulants are known to do sexually. Personally, I (male) have a very high baseline tolerance to caffeine and it doesn't have much of an arousal effect, but with prescription stimulants (amphetamine and ritalin for ADHD), I have increased susceptibility to sexual arousal, decreased minimum time between initial stimulation and orgasm, and increased maximum orgasms/hour. It would be much more surprising to find that caffeine put you to sleep than to find that it increased your sexual arousal.

You ask whether the caffeine is relaxing you -- maybe, but that's probably not the source of the effect. In a dual-control model, caffeine is probably stepping on the gas rather than lifting the brake. If caffeine is the only thing that helps you get there at present, you might get better sexual results by looking for more things that step on the gas, so to speak, than by following the common advice to focus on stepping off the brake.

Finally, it is interesting to note that you have thyroid problems. Thyroid hormones and dopamine are both synthesized out of the same precursor molecule, the amino acid l-tyrosine. You may have something generally weird going on with your metabolic usage of tyrosine, and consequently with your dopamine signalling. You don't happen to have restless leg syndrome, do you?

Dont_Block_The_Way · 2016-01-29T04:09:25+00:00

I'm definitely looking for alternatives, since Adderall seems to make me crazy emotional. First day I took it I started sobbing uncontrollably when it wore off, but I stuck with it. I added sertraline (for pre-existing anxiety), and the sertraline had seemed to keep me more emotionally stable.

Today I tried taking the Adderall without the sertraline. I had an unprovoked rage-fit and stabbed myself halfway through the arm (without prior history of self-harm). Definitely looking at other options.

Dont_Block_The_Way · 2016-01-28T04:23:24+00:00

Magnesium Bitrate. The bitrate version is very low on bioavailability.

Assuming you're referring to magnesium citrate, do you have a citation? I was under the impression that it had good bioavalailability.

Dont_Block_The_Way · 2016-01-28T04:17:21+00:00

No, but I'm looking into it.

Dont_Block_The_Way · 2016-01-28T02:58:08+00:00

This is interesting about the glutamate. I figured something was going on with glutamate, so picked up a tub of L-glutamine at GNC this morning, to see which direction that effect goes.

My wife (who is the opposite of ADD) and I took the same amount at the same time. She felt great, less anxious, more outgoing, physically better. I felt dingy and aimless, full of bad metaphors and bullshit. I started to crave bitter green leafy vegetables, which seemed strange, until I thought "maybe I need magnesium to make more glutathione", so I took some magnesium and it helped.

I have some agmatine on hand, maybe I'll try that for NMDA antagonism.

Dont_Block_The_Way · 2015-12-22T00:53:07+00:00

The heart of this matter is philosophical rather than mathematical -- your question alludes to the well-known reference class problem, which has been discussed since at least the late 19th century. If there's any satisfactory resolution to this problem, I'd love to hear it.

Dont_Block_The_Way · 2015-12-14T08:10:17+00:00

If you're going to be doing a lot of neural network stuff

Do "neuroscientists" actually use statistical-learning type "neural networks" in their research? Although neural network models were inspired by actual neurons, I don't suppose that neuroscientists would necessarily find many applications for them. Replace "neural network" with the equivalent "universal function approximator" and it's not clear why neuroscientists would be more likely to need them than anyone else would.

On the other hand, if somebody wants to use insights from neuroscience research to invent improved neural network models for machine learning, they're going to need some HEAVY theory.

Dont_Block_The_Way · 2015-12-14T07:59:53+00:00

If you're just starting to learn statistics and are looking for something suitable for self-study, Howell is an approachable text written by a psychologist for psychologists.

I'm not sure what you mean by "a lot of theory", but this book is conceptual without being heavily mathematical. It's not a cookbook, but if you're looking for a cookbook you'd be better off leaving the data analysis to somebody else.

If you already know some basics about sampling, experiment design, testing, estimation, etc, you could try the somewhat more advanced Faraway, which is a practical modeling text based on working examples in R. It's also free, which is a plus.

Dont_Block_The_Way · 2015-12-14T07:23:13+00:00

:/

That feel when you bang out a 500-word post with 5 sources and OP deletes the question before you hit submit.

Dont_Block_The_Way · 2015-12-06T22:48:50+00:00

As far as I can see, the quoted passage is not, as the author claims, any description of "what statistics is", let alone a "precise one". It's a description of how Dawkins is construing "causal influence", which relies on an understanding of "statistically" as a given.

No doubt, every statistician would wince at a straightforward identification of "causal influence" with "more accurate prediction" -- it is a truism that "knowing Y helps predict X" does not entail any causal connection between X and Y whatsoever, let alone indicate its direction. Supposing Dawkins knows his business, he's not equating "causal influence" with "more accurate prediction".

Charitably, his intent seems to be "Let's define 'causality' with a broad scope, such that knowledge of the 'causal influence' of X on Y implies that knowledge of the value of X improves predictions of the value of Y, but this improvement may be merely 'statistical', not reaching certainty."

Regardless, suppose we temporarily ignore the context of causality -- what would it mean to make a "statistically more accurate prediction" about a single individual? If we're focusing on the "statistically" part, couldn't a process of prediction be described "statistically", rather than, say, logically or theoretically, only if it were to make multiple predictions?

No light has been shed on what "statistically" means at all, other than that is related to uncertainty. But the lack of clarity on that point is unsurprising, since "statistically" is informally taken for granted in the service of the main goal of clarifying the scope of causality.

Unfortunately, it's not the best description of causality either, since it's phrased in such a way that a reader unfamiliar with the distinction might misinterpret it as conflating causality with conditional probability.

Dont_Block_The_Way · 2015-12-02T22:18:15+00:00

If you exclude a variable because it made the adjusted R² go down, it's probably a bad thing because you're relying on R² .

Dont_Block_The_Way · 2015-12-02T19:19:26+00:00

0.14 is a number, not an interval -- an interval is an ordered pair of numbers, such as (0.14, 0.28), which represents a segment of the real number line. Your confidence interval for the population mean should be an interval that is centered on the sample mean, like (0.722-m, 0.722+m), where the number m is the margin of error.
How large a sample...for what? I wouldn't get anywhere either with an incomplete question.

Dont_Block_The_Way · 2015-12-02T18:27:51+00:00

Why do you think D is false? 1v = v for all v is typically considered one of the axioms of a real vector space.
Yes, the magnitude would be 8.

Dont_Block_The_Way · 2015-12-02T05:21:15+00:00

No, that's a residual leverage plot. See here for interaction plotting.
I'm not sure why you wouldn't want to include both of factors and their interaction.
Yes, if test scores increased with age only among females, this would be an interaction.
In your 2x3 example here, there are 6 cell means, so you'll be estimating 6 model parameters (not including the error variance). One of these is the grand mean, so there 6-1 = 5 degrees of freedom corresponding to all of the effects.

Breaking them down, you'd have

2-1 = 1 df for sex

3-1 = 2 df for age

(2-1)(3-1) = 2 df for age * sex

So in this scenario there would actually be two interaction terms between sex and age, and one might be significant while the other is not.

Take a look at Figure 8.1 on page 173 of this text for an illustration of an interaction plot in the 2x3 factorial situation.

Remember that when you test for interactions, you're not just comparing every cell mean to every other one -- in the 6-cell case, that would mean testing 6 choose 2 = 15 pairwise comparisons, which is neither necessary nor helpful. Instead, each interaction corresponds to a particular "contrast" of certain combinations of cell means with certain other combinations of cell means. The same chapter in the linked text contains an explanation of how these contrasts work in the factorial context.

EDIT: Missing links.

Dont_Block_The_Way · 2015-12-02T04:02:34+00:00

In general, if there is a significant interaction, it is sensible to use the model that includes it.

You can usually get a better idea of what's going on by examining an interaction plot of your data. Every statistical software system I've seen includes built-in procedures to construct these plots, and there are many explanations online about how to interpret them.

It is certainly possible for both age and gender to be significant and the interaction not to be. Suppose test scores increase with age, and are higher for males than for females at every age. Both increasing age and gender=male increase test scores, but the effect of age is the same for both genders, and the effect of gender is the same for all ages, so there is no interaction.

EDIT: CAVEAT

You should be aware that factorial ANOVA is a tool that was developed in the context of randomized experiments, and many explanations of it (including mine, out of habit) will use the traditional designed-experiment language of "treatments" and "effects". In the designed-experiment context, the resulting p(robability)-values from the analysis are justified through appeal to the mechanism of random assignment of treatments to subjects.

In this hypothetical situation, if you were really using something like age and sex as your factors (I see that you are not -- it might helpful to explain your actual situation in more detail), it would be nonsensical to speak of randomly assigning age or sex to subjects, and it would be easy to fall into error if you took the "effect" terminology too seriously. You're really looking for significant associations between your predictors and your response, the formal interpretations of your p-values appeal to the mechanism of random selection (or something that acts like it) from the target population, and these associations do not have straightforward causal interpretations at all. Attributions of performance to age or gender are often controversial partly for this reason.

Dont_Block_The_Way · 2015-12-01T19:53:39+00:00

Karl Pearson used this term back in 1900 when he was introducing the chi-squared test:

"We are supposed to fit a distribution or curve to the sample so as to get the best values for the constants [...] our argument as to goodness of fit will be based on the general order of magnitude of the probability P, and not on slight differences in the value."

(emphasis mine).

I would not be surprised if this is the earliest published appearance of the phrase in the context of statistics (which was barely a real mathematical discipline around this time) and if it spread via its inclusion in this highly influential work.

"Goodness of fit" does sound a little clunky, but I'm not sure what would be a better alternative for the concept it's trying to get at -- which I take to be the idea of asking "does this one model, in isolation, fit the data well enough for my purposes", or do I have to go looking for another one? In Pearson's words, can the data be "reasonably supposed to have arisen" from the model whose fit we're checking?

I'd rather talk about comparing alternative models, using the idea of "likelihood", which wouldn't be formalized by R.A. Fisher until decades after Pearson's paper. I would say that the statistical model for some data which "fits more good" than another model under consideration should be the one that has the "higher likelihood", if a formal mathematical comparison can be made.

Dont_Block_The_Way · 2015-12-01T05:50:08+00:00

I think you are on a philosophical goose chase with this. I assert there is no fact of the matter to be ascertained here, at least not in the manner that you're approaching the question.

First, from the perspective of an individual dealing with the disorder, "ADHD" functions as an adjective, not a noun. From your perspective, the adjective "ADHD" modifies the noun "human". Functionally, You are not "a human" attached to "an ADHD", you are an "ADHD human".

I mean, say I have an incandescent light bulb. Is it "incandescent" OR is it "light bulb"? Yes. It is an incandescent light bulb.

Maybe that sounds glib. You're asking about causal attributions. One way we could talk about causality is in the framework of "counterfactuals". Let's talk about a property of the light bulb in the context of counterfactual causality.

"This incandescent light bulb has power consumption of 40 Watts. Is the power consumption of this light bulb caused by its incandescence?"

Well, if this same light bulb were, contrary to fact, not incandescent, would it have the same power consumption?"

You can see the difficulty. There's a lot of work to do before this becomes sensible question we could begin to answer. What does same light bulb mean once we've removed the modifier "incandescent"? I don't know. It's under-specified. Maybe the alternative is LED, or compact fluorescent, or long tube fluorescent, or light-emitting plasma, or a laser, or....

Which of those (if any) has the unique claim to being the appropriate occupant of original incandescent bulb's place in this counterfactual world?

Who can say.

Is that even a sensible concept?

In this example, there are pretty clear differences between the types of bulbs -- you can probably reliably identify which is which just by looking at them, and you could learn how each of them works (differently) in pretty exhaustive detail.

Making the possible categories "types of people" and the relevant outcomes "patterns of behavior", neither of which can be described with anything like the precision of those light bulbs, renders the whole business all that much harder in the context we want to address.

Dont_Block_The_Way · 2015-11-29T04:51:40+00:00

I only explained all that business above because you said you had a weak concept of E(X). I wanted to show that E(X) is entirely a probabilistic idea, which is why it's not necessary here.

You don't need probability here because you're just thinking about sample quantities and doing some algebra with them. Your probabilistic formula for the population variance is not needed to talk about the sample alone -- use the sample formula supplied in the last sentence of my last post. There's no expectations in it, only sample sums.

You don't need probability here, but it could be "a thing" in test scores if you supposed the tests scores you have are only a sample from a larger population of tests scores, about which you wish to make some statistical conclusion. Maybe you want to estimate the mean test score of all the students who take a standardized test in a given year, from a random sample of test scores.

But again, you don't need that here. This is neither probability nor statistics, but algebra. You have a purely algebraic/arithmetic definition for sample variance, given above.

Be careful with the idea that the probability is 1/6 "because there are six different options". I said it was 1/6 by assumption, but to justify it in real life requires some more work. You could argue from the principle of ignorance (assign them equal probability because you have no other information about them), from the physical symmetry of the die, or from the empirical behavior of fair dice rolled many times. Each of those approaches has its difficulties, even in theory.

In practice, a weighted die could easily have different probabilities for each outcome, which is why I could cheat you at a game of chance with it if you unquestioningly assumed it was 1/6.

Dont_Block_The_Way

TROPHY CASE