о методе максимального правдоподобия и информации Фишера https://habr.com/ru/articles/830326/
https://stats.stackexchange.com/questions/357963/what-is-the-difference-between-cross-entropy-and-kl-divergence
https://mattblackwell.github.io/gov2002-book/
https://medium.com/@lucassamba/3-probability-questions-i-was-asked-in-walmart-data-scientist-interview-f3cddba746d1
The waiting time paradox: why is my bus always late? (2018)
https://jakevdp.github.io/blog/2018/09/13/waiting-time-paradox/
https://news.ycombinator.com/item?id=41300111
https://habr.com/ru/companies/kuper/articles/827448/ Cтатистические критерии для начинающих
https://stats.stackexchange.com/questions/357963/what-is-the-difference-between-cross-entropy-and-kl-divergence
https://news.ycombinator.com/item?id=42419224
book: https://matheusfacure.github.io/python-causality-handbook/landing-page.html
pycausalimpact: https://pypi.org/project/pycausalimpact/
https://habr.com/ru/articles/832466/
https://habr.com/ru/companies/otus/articles/823490/
https://habr.com/ru/articles/801101/
https://habr.com/ru/articles/807051/ Индуктивная статистика: доверительные интервалы, предельные ошибки, размер выборки и проверка гипотез
https://habr.com/ru/articles/217545/ Как правильно лгать с помощью статистики
Разбираемся в ROC и AUC https://habr.com/ru/companies/otus/articles/809147/
https://habr.com/ru/companies/aktiv-company/articles/823510/
https://habr.com/ru/articles/912270/
Три парадокса теории вероятностей https://habr.com/ru/articles/912270/
using SQL https://github.com/jchester/spc-kit
https://news.ycombinator.com/item?id=39612775
https://extremelearning.com.au/how-to-generate-uniformly-random-points-on-n-spheres-and-n-balls/
https://news.ycombinator.com/item?id=39606371
Во всех этих задачах подразумевается равномерное распределение по поверхности или по объему
https://nabbla1.livejournal.com/95999.html
https://www.alexirpan.com/2015/08/23/simulating-a-biased-coin-with-a-fair-one.html
https://www.quora.com/How-can-I-simulate-a-die-given-a-fair-coin
https://www.quora.com/Can-you-use-a-single-coin-to-simulate-probabilities-like-frac-1-11-Whats-the-minimum-number-of-flips-on-average
https://www.youtube.com/watch?v=lORQ_wt2MZY
https://math.stackexchange.com/questions/3834837/how-to-use-a-fair-coin-to-simulate-any-probability-p-of-winning
https://math.stackexchange.com/questions/3568428/get-1-3-probability-from-a-coin-in-minimal-mean-of-the-amount-of-flips
unbiased coin has P(H) = P(T) = 1/2.
To create the event that the question desires, do follow:
first, understand that P(HH) = P(HT) = P(TH) = P(TT) = 1/4
if you flip coin 2 times in a row:
1) HH happens -> do sth
2) HT happens -> do another stuff
3) TH happens -> do other stuff
4) if TT happens -> reroll coin 2 times again and go back .
This guarantees that we only observe 1/3 events.
Toss the unbiased coin thrice. Given the outcome is not both tails ( T T) ,
then the outcome of both heads (H H) has probability 1/3.
https://arxiv.org/pdf/2207.02296.pdf A Tutorial on the Spectral Theory of Markov Chains
https://www.jeremykun.com/2015/04/06/markov-chain-monte-carlo-without-all-the-bullshit/
https://news.ycombinator.com/item?id=43700633 Markov Chain Monte Carlo Without All the Bullshit (2015) (jeremykun.com)
https://www.statlearning.com/ An Introduction to Statistical Learning with Pyton
https://drive.google.com/file/d/1VmkAAGOYCTORq1wxSQqy255qLJjTNvBI/edit?pli=1 Introduction to probability 2nd edition
https://www.thegreatcourses.com/courses/learning-statistics-concepts-and-applications-in-r Learning Statistics: Concepts and Applications in R
Great links to stat resources https://news.ycombinator.com/item?id=37854846
https://jasp-stats.org/
https://habr.com/ru/articles/853560/
https://habr.com/ru/articles/802435/
https://xcelab.net/rm/statistical-rethinking/ A Bayesian Course with Examples in R and Stan (& PyMC3 & brms & Julia too
https://users.aalto.fi/~ave/ROS.pdf Regression and Other Stories
https://openintro-ims2.netlify.app/
https://brilliant.org/courses/statistics/
https://github.com/joelparkerhenderson/queueing-theory
https://news.ycombinator.com/item?id=37532439
https://www.cantorsparadise.com/whats-the-probability-of-1-appearing-as-the-first-digit-of-a-number-41f2fcd781c7 frequency of 1st digit in the number - Benford’s law
Hight dimentional probability - Roman Vershinin: https://www.math.uci.edu/~rvershyn/papers/HDP-book/HDP-book.pdf
https://mltechniques.com/resources/
Extreme events in dynamical systems and random walkers: A review https://arxiv.org/abs/2109.11219
https://habr.com/ru/post/678604/ Viterbi
https://www.toptal.com/algorithms/metropolis-hastings-bayesian-inference
https://habr.com/ru/company/skillfactory/blog/674880/
https://habr.com/ru/company/X5Tech/blog/679842/
https://habr.com/ru/company/mygames/blog/677074/
https://github.com/matteocourthoud/Blog-Posts
Cook book
https://pages.cs.wisc.edu/~tdw/files/cookbook-en.pdf
P R O B A B I L I S T I C N U M E R I C S
C O M P U TA T I O N A S M A C H I N E L E A R N I N G
https://www.probabilistic-numerics.org/assets/ProbabilisticNumerics.pdf
https://lukepereira.github.io/notebooks/documents/2021-moduli-attention/main.pdf
https://habr.com/ru/post/658707/
https://habr.com/ru/company/otus/blog/658311/
https://towardsdatascience.com/detect-change-points-with-bayesian-inference-and-pymc3-3b4f3ae6b9bb
https://towardsdatascience.com/a-gentle-intro-to-conjugate-priors-8be6ac0d31f6
https://habr.com/ru/post/598979/
https://www.edx.org/bio/elena-moltchanova . Bayes stat with R
https://betterexplained.com/articles/an-intuitive-and-short-explanation-of-bayes-theorem/
P(A/B) = P(A) * P(B/A) / P(B)
posterior = prior * (likelihood /marginal)
Marginal probability (probability of the evidence, under any circumstance)
Suppose you get a positive test result. What are the chances you have cancer?
The chances of a true positive = chance you have cancer * chance test caught it = 1% * 80% = .008
The chances of a false positive = chance you don’t have cancer * chance test caught it anyway = 99% * 9.6% = 0.09504
The chance of getting a real, positive result is .008. The chance of getting any type of positive result is the chance of a true positive plus the chance of a false positive (.008 + 0.09504 = .10304).
So, our chance of cancer is .008/.10304 = 0.0776, or about 7.8%.
http://allendowney.github.io/ThinkBayes2/index.html
https://austinrochford.com/posts/2021-06-10-lego-pymc3.html
https://pub.towardsai.net/bayesian-inference-beyond-estimating-statistical-models-4b2f78c7f090
https://www.cs.ox.ac.uk/people/nando.defreitas/publications/BayesOptLoop.pdf Bayes
http://www.numericalexpert.com/blog/online_stat/ single pass for (variance, skewness, kurtosis, covariance)
http://www.numericalexpert.com/tutorials.php
precision and recall https://habr.com/ru/post/661119/
https://sakshamgulati123.medium.com/an-intuitive-guide-to-various-statistical-tests-d8148105eeca
The number of permutations of n distinct objects is n!
сочетанием из n по k называется набор из k элементов, выбранных из n-элементного множества, в котором не учитывается порядок элементов.
combination is a selection of items from a set that has distinct members, such that the order of selection does not matter
If the set has n elements, the number of k-combinations, denoted as C_{k}^{n}} C_{k}^{n}, is equal to the binomial coefficient C= n!/k!(n-k)!
размеще́нием (из n по k) называется упорядоченный набор из k различных элементов из некоторого множества различных n элементов. В отличие от сочетаний, размещения учитывают порядок следования предметов.
A= n!/k!(n-k)!
https://patsy.readthedocs.io/en/latest/ Describing statistical models in Python
https://habr.com/ru/post/681218/. statmodels
if we have some variable y, and we want to regress it against some other variables x, a, b, and the interaction of a and b, then we simply write:
patsy.dmatrices(“y ~ x + a + b + a:b”, data)
https://cdanielaam.medium.com/essential-mathematical-equations-for-predictive-models-fcb79630ec96
https://habr.com/ru/post/585232/ Получаем кривую плотности распределения вероятности
https://habr.com/ru/post/587372/ Получаем кривую плотности распределения вероятности случайного процесса
https://habr.com/ru/post/556856/ Python и статистический вывод: часть 4
https://habr.com/ru/post/562380/ Погружаемся в статистику вместе с Python
https://towardsdatascience.com/probability-distributions-with-pythons-scipy-3da89bf60565
https://towardsdatascience.com/statistical-modelling-with-python-the-three-must-know-s-modules-79fa393e5640
https://www.kdnuggets.com/2021/09/advanced-statistical-concepts-data-science.html
https://www.kdnuggets.com/2021/09/determine-best-fitting-data-distribution-python.html
https://towardsdatascience.com/a-practical-introduction-to-9-regression-algorithms-389057f86eb9
https://towardsdatascience.com/deep-diving-statistical-distributions-with-python-for-data-scientists-a0a4badc8d1a
https://towardsdatascience.com/random-seed-numpy-786cf7876a5f
https://towardsdatascience.com/practical-guide-to-common-probability-distributions-in-machine-learning-487f6137625
https://towardsdatascience.com/practical-guide-to-common-probability-distributions-in-machine-learning-part-2-5bcb910218c0
https://towardsdatascience.com/statistics-in-python-generating-random-numbers-in-python-numpy-and-sklearn-60e16b2210ae
https://towardsdatascience.com/mathematical-statistics-a-rigorous-derivation-and-analysis-of-the-wald-test-score-test-and-6262bed53c55
https://towardsdatascience.com/union-of-probabilistic-event-groups-b415d23e1a62
https://www.statlearning.com/
https://www.crosstab.io/
https://xcelab.net/rm/statistical-rethinking/
http://bayes.cs.ucla.edu/WHY/ The Book of why
five number summary in statistics: mean, median, standard deviation, 25th percentile and 75th percentile.
interquartile range - IQR is 75th - 25th, aka, the middle-50%
https://habr.com/ru/post/548104/ ТЕСТ МАННА-УИТНИ-УИЛКОКСОНА И SCORE-ФУНКЦИИ
https://seeing-theory.brown.edu/index.html
http://www.jerrydallal.com/LHSP/LHSP.HTM
https://www.stat.berkeley.edu/~aditya/resources/AllLectures2018Fall201A.pdf
https://habr.com/post/265321/ . statistical disributions
https://seeing-theory.brown.edu/index.html . Visual probability and stat
https://textbooks.opensuny.org/introduction-to-the-modeling-and-analysis-of-complex-systems/
https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf
https://github.com/Quanteeks/Statistics-lectures/blob/master/book.pdf
https://queueing-tool.readthedocs.io/en/latest/
https://www.youtube.com/watch?v=pYxNSUDSFH4 Probability vs likehood
https://www.dynatrace.com/news/blog/why-averages-suck-and-percentiles-are-great/
https://www.youtube.com/watch?v=coNDCIMH8bk
“How NOT to Measure Latency”
https://www.infoq.com/presentations/latency-response-time/
https://github.com/astralord/Statistics-lectures/blob/master/book.pdf
https://openintro-ims.netlify.app/ Modern stat book
https://www.dynatrace.com/news/blog/why-averages-suck-and-percentiles-are-great/
https://www.infoq.com/presentations/latency-response-time/
https://towardsdatascience.com/all-probability-distributions-explained-in-six-minutes-fe57b1d49600
https://www.maa.org/sites/default/files/pdf/ebooks/GTE_sample.pdf
https://github.com/telmo-correa/all-of-statistics
In the case of normally distributed data, the three sigma rule means that roughly 1 in 22 observations will differ by twice the standard deviation or more from the mean, and 1 in 370 will deviate by three times the standard deviation
https://statmodeling.stat.columbia.edu/2020/12/09/what-are-the-most-important-statistical-ideas-of-the-past-50-years/
How percentile approximation works and why it's more useful than averages https://news.ycombinator.com/item?id=28526966
https://www.crosstab.io/topics/survival-analysis
https://www.crosstab.io/articles/survival-analysis-applications
https://www.codingthepast.com/2024/11/28/Python-z-score.html
https://habr.com/ru/companies/otus/articles/793678/
https://statisticsbyjim.com/basics/z-score
https://towardsdatascience.com/a-new-coefficient-of-correlation-64ae4f260310
https://gwern.net/doc/statistics/order/2020-chatterjee.pdf
https://habr.com/ru/post/683442/
https://habr.com/ru/company/glowbyte/blog/686398/
https://arxiv.org/abs/2206.15475
https://arxiv.org/abs/2206.15475
https://habr.com/ru/company/ods/blog/544208/
BOOK: Causal Inference for The Brave and True
https://matheusfacure.github.io/python-causality-handbook/landing-page.html
BOOK: Causal Inference in Python https://www.oreilly.com/library/view/causal-inference-in/9781098140243/
https://microsoft.github.io/dowhy/
https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/
https://www.bradyneal.com/which-causal-inference-book
https://yanirseroussi.com/causal-inference-reading-list/
https://www.inference.vc/causal-inference-4/
https://habr.com/ru/post/558836/
https://habr.com/ru/companies/X5Tech/articles/807001/ T-test
https://davidbergkamp.com/two-way-anova-tukeys-honest-difference-test/
https://towardsdatascience.com/statistics-in-python-using-anova-for-feature-selection-b4dc876ef4f0