Bernoulli, Moab-Laplace theorem; Kolmogorov criterion; Bernoulli scheme; Bayes formula; Chebyshev inequalities; Poisson distribution law; Fisher, Pearson, Student, Smirnov etc. theorems, models, simple language, without formulas. - page 4
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I see. Chinese poetry is better read in the original.
But this, this is not a fish in a bay.
It's not for housewives, it's for students.
You're not a five-star chef, but it's quite, I stress, edible and nutritious.
Back to our rams.
One last question: what's on the axes of the chart?
Man, I feel for you.
Alexey, could you place the scholars in the title of the topic, in a certain order. From the general to the particular or in order of complexity, importance, usefulness, etc... regarding the application of their teachings to the price range. Afterwards, justify your choice of criterion(s).
Include a Gamma distribution, please, because the Pearson distribution is a special case of it, and the Poisson distribution function, as the closest to the price series, is also expressed as a Gamma distribution and can even be normalised.
IMHO, the main thing is to deal with the indicator and sovom based on it at n=n - it is a general case, moreover, we have expressions in eexcel! So... - by the way.
Plz, Alexei arranged everything under his arm.
Then the desserts.
Of course, I'm not pulling, at least I'd like to make some chowder... But it's not like anyone's going to help me yet. What kind of cook is there in a five-star if there's only one?
One last question: what's on the axis of the graph?
On the horizontal (abscissa) is the number of successes in the overall test series. On the vertical (ordinates) is the relative frequency, i.e. the proportion of successes in the total number of trials.
I forgot to add: binomial distribution becomes similar to normal distribution not only when n*p >= 5, but also under additional condition: p should not be too close to 1. Well, say, at p~0.5, n~10 is already quite similar.
yosuf: Please include Gamma distribution, we need to deal with it, because Pearson distribution is its special case, and Poisson distribution function, as the closest to the price series, is also expressed through Gamma distribution and even it can be brought to a normal distribution.
Start by yourself and try to explain to homebred humanitarians why they need Pearson distributions. I didn't even know they existed before you addressed me...
And explain why they need to express Poisson and normal (both are quite practical distributions) through the spherical horse "Pearson distribution".
But I will think about the Gamma distribution.
Alexey, could you place the scientists in the title of the thread, in a certain sequence. From the general to the particular, or in order of complexity, importance, usefulness, etc... regarding the application of their teachings to the price series.
It's not that simple. But the Kolmogorov criterion should definitely be somewhere near the end. Chebyshev's inequalities are only needed for fairly rough estimates.
Let everything remain as it is, and we will choose what we can explain on the basis of what we have learned.
Thank you very much, Alexey.
I'll ask around a bit more later.
I think the best place to start is with the base.
Ask yourself how you get a normal distribution.
Or for example - there are 10 random number generators. That generate a uniform distribution, that is, it is ONE and independent random variable. For example a cube with six sides. So how do you get a NORMAL distribution out of it? How do you get a NATURAL normal distribution from an artificial one?
Who knows the answer?
Or for example - there are 10 random number generators. Which generate a uniform distribution, i.e. it is ONE and independent random variable. For example a cube with six sides. So how do you get a NORMAL distribution out of it? How do you get a NORMAL distribution from an artificial one?
You said it. There are several methods of generating a normal distribution - here, for example. But they also rely on a uniform one as a base.
One can, of course, also "directly". We will first generate a normal distribution and then apply the function inverse of the integral function of the normal distribution to the results. But the problem is the same: it is necessary to first generate a uniform one.
Good uniform generators are described in the literature. And the last 64-bit one for Windows is also seemingly good, much better than the standard C-shaped one.
But the standard one is not so bad either. In any case, the effects of its "unnaturalness" are not so easy to detect.
Natural normal - what do you need it for, S?