MindMap Gallery What is Probability
Discover the fascinating world of probability, where uncertainty meets mathematical precision. This introduction explores key concepts, starting with definitions and interpretations of probabilityclassical, frequentist, and Bayesian. We delve into fundamental laws, including Kolmogorov's axioms, and essential properties like conditional probability and independence. Learn about the law of total probability and Bayes' theorem, which are crucial for updating beliefs based on new evidence. Finally, connect probability to random variables and distributions, highlighting their significance in real-world applications. Join us on this journey to understand how probability shapes our understanding of chance and decision-making.
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What is Probability
Definition and intuition
Probability as a measure of uncertainty
Quantifies how likely an event is to occur
Values range from 0 (impossible) to 1 (certain)
Core components
Experiment/Trial: a repeatable process with uncertain outcome
Outcome: a single possible result of the experiment
Sample space (Ω): set of all possible outcomes
Event (A): subset of Ω (collection of outcomes)
Interpretations of probability
Classical (equally likely outcomes)
P(A) = (number of favorable outcomes) / (number of possible outcomes)
Best for symmetric situations (e.g., fair dice, fair cards)
Frequentist (long-run relative frequency)
Probability as the limiting proportion over many repeated trials
Requires repeatability and stable conditions
Subjective/Bayesian (degree of belief)
Probability reflects informed belief given current information
Updated using observed data (Bayes’ rule)
One concept, three lenses—symmetry (classical), repetition (frequentist), belief-updating (Bayesian).
Basic probability laws (axioms)
Kolmogorov axioms
Non-negativity
For any event A: P(A) ≥ 0
Normalization
P(Ω) = 1
Additivity (for mutually exclusive events)
If A ∩ B = ∅ then P(A ∪ B) = P(A) + P(B)
Extended: for countably many pairwise disjoint events A₁, A₂, …:
P(⋃ Aᵢ) = Σ P(Aᵢ)
Fundamental properties derived from the axioms
Probability of the empty set
P(∅) = 0
Bounds
0 ≤ P(A) ≤ 1
Complement rule
Aᶜ = Ω \ A
P(Aᶜ) = 1 − P(A)
Monotonicity
If A ⊆ B then P(A) ≤ P(B)
Difference of events
P(B \ A) = P(B) − P(A ∩ B)
Addition laws (union rules)
Two-event addition rule (general)
P(A ∪ B) = P(A) + P(B) − P(A ∩ B)
Mutually exclusive (disjoint) events
If A ∩ B = ∅ then P(A ∪ B) = P(A) + P(B)
Inclusion–exclusion principle (multiple events)
Three events
P(A ∪ B ∪ C) = P(A)+P(B)+P(C)
− P(A∩B) − P(A∩C) − P(B∩C)
+ P(A∩B∩C)
General form
Alternating sum of intersections of increasing order
Conditional probability
Definition
For P(B) > 0: P(A | B) = P(A ∩ B) / P(B)
Meaning
Probability of A given that B has occurred
Restricts the “universe” from Ω to B
Relationship to intersection
P(A ∩ B) = P(A | B) P(B) = P(B | A) P(A)
Multiplication laws (product rules)
Two events
P(A ∩ B) = P(A) P(B | A)
P(A ∩ B) = P(B) P(A | B)
Chain rule (multiple events)
P(A₁ ∩ A₂ ∩ … ∩ Aₙ)
= P(A₁) · P(A₂ | A₁) · P(A₃ | A₁∩A₂) · … · P(Aₙ | A₁∩…∩Aₙ)
Independence
Definition (two events)
A and B are independent if:
P(A ∩ B) = P(A) P(B)
Equivalent forms (when probabilities are nonzero)
P(A | B) = P(A)
P(B | A) = P(B)
Pairwise vs mutual independence (more than two events)
Pairwise independence
Every pair (Aᵢ, Aⱼ) is independent
Mutual independence
For every subset of events, the probability of their intersection equals the product of their probabilities
Stronger than pairwise independence
Independence vs mutual exclusivity (common confusion)
Mutually exclusive: cannot occur together (A ∩ B = ∅)
Independent: occurrence of one does not change probability of the other
If events are mutually exclusive and both have positive probability, they cannot be independent
Law of total probability
Partition of the sample space
Events B₁, …, Bₖ such that:
Bᵢ are disjoint: Bᵢ ∩ Bⱼ = ∅ for i ≠ j
Cover the space: ⋃ Bᵢ = Ω
Typically P(Bᵢ) > 0
Formula
P(A) = Σ P(A | Bᵢ) P(Bᵢ)
Use cases
“Mixture” or “weighted average” across cases/scenarios
Computing probabilities when direct calculation is hard
Bayes’ theorem (Bayes’ rule)
Two-event form
For P(B) > 0:
P(A | B) = P(B | A) P(A) / P(B)
With a partition (multiple hypotheses)
P(Bⱼ | A) = P(A | Bⱼ) P(Bⱼ) / Σ P(A | Bᵢ) P(Bᵢ)
Key terms
Prior: P(Bⱼ)
Likelihood: P(A | Bⱼ)
Evidence/Marginal likelihood: P(A)
Posterior: P(Bⱼ | A)
Practical meaning
Updates beliefs after observing evidence
Random variables and probability distributions (connection to laws)
Random variable (X)
Function mapping outcomes to numbers: X: Ω → ℝ
Discrete distributions
Probability mass function (pmf): p(x) = P(X = x)
Laws
p(x) ≥ 0
Σ p(x) = 1
P(X ∈ S) = Σ_{x∈S} p(x)
Continuous distributions
Probability density function (pdf): f(x)
Laws
f(x) ≥ 0
∫ f(x) dx = 1
P(a ≤ X ≤ b) = ∫_a^b f(x) dx
Note
For continuous variables: P(X = c) = 0
Cumulative distribution function (cdf)
F(x) = P(X ≤ x)
Properties
Non-decreasing, right-continuous
lim_{x→-∞} F(x)=0, lim_{x→∞} F(x)=1
Common probability rules in practice (quick reference)
Complement
P(not A) = 1 − P(A)
Union
P(A or B) = P(A)+P(B)−P(A and B)
Conditional
P(A given B) = P(A and B) / P(B)
Product
P(A and B) = P(A) P(B|A)
Independence
If independent: P(A and B) = P(A) P(B)
Total probability
P(A) = Σ P(A|Bᵢ)P(Bᵢ)
Bayes
P(Bⱼ|A) ∝ P(A|Bⱼ)P(Bⱼ)
Most computations reduce to complement, union, conditioning, products, and Bayes/total-probability for “case splits.”
Typical pitfalls and checks
Confusing P(A ∩ B) with P(A | B)
Intersection: both happen
Conditional: A happens under the condition B happened
Forgetting overlap in unions
Must subtract P(A ∩ B) unless disjoint
Treating mutually exclusive events as independent
Usually incorrect unless one has probability 0
Not verifying conditions
Conditional probability requires P(B) > 0
Total probability requires a valid partition
Sanity checks
Result must be within [0, 1]
Symmetry/equally likely assumptions must be justified