More on Axiomatic Approach to Probability Data Science and AI Lecture Series By Bindeshwar Singh Kushwaha Statement of the First Proof Prove: \( P(A \cap B^c) = P(A) – P(A \cap B) \) This formula expresses the probability of \( A \) occurring without \( B \). It uses the complement rule and properties of […]
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Axiomatic Approach to Probability Data Science and A.I. Lecture Series Axiomatic Approach to Probability Definition: Let \( S \) be a sample space for a random experiment. Let \( A \) be an event that is a subset of \( S \). \( P(A) \) is called a probability function if it satisfies the
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Set Operations and Important Laws Data Science and A.I. Lecture Series Set Operations Union Definition: \[ A \cup B = \{x : x \in A \text{ or } x \in B\} \] Examples: \( A = \{1, 2, 3\}, B = \{3, 4, 5\} \implies A \cup B = \{1, 2, 3, 4, 5\}
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Introduction to Sets and Type of Sets Data Science and A.I. Lecture Series Introduction A set is a well-defined collection of distinct objects. Examples of collections: Books in a library. Natural numbers that are factors of a given number. States in a country. Sets are fundamental in mathematics and are used in many areas,
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Probability Examples Related to Combinations Data Science and A.I. Lecture Series Author: Bindeshwar Singh Kushwaha Example: Drawing Two Cards from a Well-Shuffled Pack of Cards Find the probability of the following scenarios: One red and one black card. Both cards of the same suit. One jack and one king. One red card and one card
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Examples and Theorem Related to Combinations Data Science and A.I. Lecture Series Author: Bindeshwar Singh Kushwaha Theorem: Relationship Between Permutations and Combinations Theorem: The number of permutations of \(n\) different objects taken \(r\) at a time is related to the number of combinations by: \[ P^n_r = C^n_r \cdot r! \] where \(0 < r
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Understand Combinations Data Science and A.I. Lecture Series Introduction to Combinations A combination is a selection of items where the order does not matter. Example: Selecting 2 players from a group of 3 players (X, Y, Z). Possible combinations: XY, XZ, YZ. Formula for combinations: \[ \binom{n}{r} = \frac{n!}{r!(n-r)!}, \quad 0 \leq r \leq
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Understanding Permutations Data Science and A.I. Lecture Series Author: Bindeshwar Singh Kushwaha PostNetwork Academy Introduction to Permutations A permutation is an arrangement of objects in a specific order. The order of arrangement is crucial in permutations. Example: Arranging the letters of the word “ABC”. Total permutations = $3! = 6$. Key Formula for Permutations The
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Probability Problems Based on Classical Definition of Probability Data Science and A.I. Lecture Series Questions What is the total number of outcomes (sample space)? How do we determine favorable cases? How do probability rules apply to the problem? Example: Throwing Two Dice Find the probability of: A doublet (same number on both dice) Sum
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Probability Problem: Throwing a Fair Die Data Science and A.I. Lecture Series Problem Statement A fair die is thrown. Find the probability of: A prime number An even number A number multiple of 2 or 3 A number multiple of 2 and 3 A number greater than 4 Step 1: Sample Space Sample Space:
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