Probability for Artificial Intelligence | NUMPY Probability distribution
Probability is a mathematical representation of how likely an event is to happen. For example, when you toss a coin, the probability of getting heads is 50%, as you have an equal chance of getting heads or tails.
Basic Probability: Probability, Sample Space, and Probability of Events.
Combinators: Arrangements, Combinator Selection (Permutation and Combination).
Conditional Probability and Bayes' Theorem.
Probability Distributions: Normal, Binomial, Poisson, Uniform, Logistic, Multinomial, Exponential, Chi-Square, Rayleigh, and Pareto distributions.
Random Variables: Discrete and Continuous Random Variables, Probability Density Function, and Cumulative Distribution Function.
Central Limit Theorem, Hypothesis Testing, Monte Carlo Simulator, and Markov Chain.
Probability is a mathematical representation of the likelihood of an event occurring. It is expressed as the ratio of the number of favorable outcomes to the total outcomes in the sample space. For example, in a coin toss, there are two possible outcomes (heads or tails), making the probability of getting heads 1/2 or 50%.
Complementary events refer to the non-occurrence of a particular event. If we denote the probability of event A as P(A), then the probability of the complementary event (not A) is represented as P(A'). It can be calculated as 1 - P(A). For instance, in a coin toss, the probability of getting heads is 50% (P(A)), and the probability of getting tails (complementary event) is also 50% (1 - P(A)).
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