कक्षा 9–12Probability & Statisticsपाठ उपलब्ध
Conditional probability and independence
Addition and multiplication rules, conditional probability, independence, Bayes' theorem in simple cases, and probabilities computed by counting.
math.prob.probability-rules
इस पर आधारित
18 अनिवार्य पूर्वापेक्षाएँ (बिल्कुल नीचे तक)
आप सीखेंगे
- Compute conditional probabilities from two-way tables and tree diagrams.
- Decide whether events are independent, and distinguish independence from mutual exclusivity.
- Apply Bayes' theorem to problems such as diagnostic testing with base rates.
परिभाषा
- For P(B) > 0, the conditional probability of A given B is P(A | B) = P(A ∩ B)/P(B).
- Events A and B are independent if P(A ∩ B) = P(A)P(B).
- Events A and B are mutually exclusive if they cannot both occur, that is, A ∩ B = ∅.
प्रमेय
- P(A ∪ B) = P(A) + P(B) − P(A ∩ B).
- Bayes' theorem: P(A | B) = P(B | A)P(A)/P(B) when P(A) > 0 and P(B) > 0.
- Law of total probability: if B₁, …, Bₙ partition the sample space, P(A) = Σ P(A | Bᵢ)P(Bᵢ).
- Two mutually exclusive events with positive probabilities are never independent.
आम भ्रांतियाँ
- Confusing P(A | B) with P(B | A) (the prosecutor's fallacy).
- Ignoring base rates when interpreting a positive test result.
- Thinking 'independent' means 'mutually exclusive'.
आगे ले जाता है
वैकल्पिक खोजें
- Statistical inference Bayesian statistics uses Bayes' theorem to update beliefs about unknown quantities in the light of data.