Kolmogorov's axioms, events as sets, conditional probability and Bayes.
Probability is a measure on events: non-negative, total 1, additive over disjoint events. Conditional probability P(A|B) = P(A ∩ B)/P(B), and Bayes reverses the conditioning. Picture it: a tree diagram; multiply along branches, add across leaves. Think it: the axioms are measure theory with total mass 1, which is why the two subjects merged.
Indi sen
Bir meseläni saýla, ýa-da ýaz ýa-da özüňi surata çek. Her ädim, bir resim, jogap sorag berlençä gizlenýär.
Özüň işle
Mugt hasap her bir ders üçin notlar goşýar, tamamlanan zadyňyzyň ýazgysyny, çözen problemaňyzy bir ýerde, we bu sahypa barada sorap biljek mugallymy. Matematika özüçe her kim üçin açyk, girseň ýa girmeseň.
Eň täze düşündirme, surat, we her harpyň manysy üçin her bir simwoly basyň.
Her bir bölegiň manysy
Resim
Bilen
Mysal
Bu beýanda hem ulanylan
Nädip: Sample spaces and the axioms
Power: (5/6)^4 = 625/1296.
Simplify.
Adamlar soraýan soraglar
What is the difference between probability and statistics?
Probability goes from a known model to what the data should look like; statistics goes from data back to the model. Probability theory is the deductive half.
What does the law of large numbers promise?
That the average of many independent samples converges to the expected value. It says nothing about any single trial.