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Slides from the presentation "Probably, Definitely, Maybe" delivered at Deovxx 2014. See Parleys.com for the full video https://www.parleys.com/speaker/5148920c0364bc17fc5697a5
Probably, Definitely, Maybe
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classical, empirical, and subjective approaches to probability.conditional probability and joint probability.Bayes’ theorem.
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Slides from the presentation "Probably, Definitely, Maybe" delivered at Deovxx 2014. See Parleys.com for the full video https://www.parleys.com/speaker/5148920c0364bc17fc5697a5
Probably, Definitely, Maybe
Probably, Definitely, Maybe
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ICP - Lecture 7 and 8
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Hassaan Rahman
Global Supply Chain
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pirama2000
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pirama2000
classical, empirical, and subjective approaches to probability.conditional probability and joint probability.Bayes’ theorem.
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ConflagratioNal Jahid
Lecture on Statistics 1
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International peer-reviewed academic journals call for papers, http://www.iiste.org/Journals
Stability criterion of periodic oscillations in a (10)
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Alexander Decker
1 Probability Please read sections 3.1 – 3.3 in your textbook Def: An experiment is a process by which observations are generated. Def: A variable is a quantity that is observed in the experiment. Def: The sample space (S) for an experiment is the set of all possible outcomes. Def: An event E is a subset of a sample space. It provides the collection of outcomes that correspond to some classification. Example: Note: A sample space does not have to be finite. Example: Pick any positive integer. The sample space is countably infinite. A discrete sample space is one with a finite number of elements, { }1,2,3,4,5,6 or one that has a countably infinite number of elements { }1,3,5,7,... . A continuous sample space consists of elements forming a continuum. { }x / 2 x 5< < 2 A Venn diagram is used to show relationships between events. A intersection B = (A ∩ B) = A and B The outcomes in (A intersection B) belong to set A as well as to set B. A union B = (A U B) = A alone or B alone or both Union Formula For any events A, B, P (A or B) = P (A) + P (B) – P (A intersection B) i.e. P (A U B) = P (A) + P (B) – P (A ∩ B) 3 cA complement not A A ' A A = = = = A complement consists of all outcomes outside of A. Note: P (not A) = 1 – P (A) Def: Two events are mutually exclusive (disjoint, incompatible) if they do not intersect, i.e. if they do not occur at the same time. They have no outcomes in common. When A and B are mutually exclusive, (A ∩ B) = null set = Ø, and P (A and B) = 0. Thus, when A and B are mutually exclusive, P (A or B) = P (A) + P (B) (This is exactly the same statement as rule 3 below) Axioms of Probability Def: A probability function p is a rule for calculating the probability of an event. The function p satisfies 3 conditions: 1) 0 ≤ P (A) ≤1, for all events A in the sample space S 2) P (Sample Space S) = 1 3) If A, B, C are mutually exclusive events in the sample space S, then P(A B C) P(A) P(B) P(C)∪ ∪ = + + 4 The Classical Probability Concept: If there are n equally likely possibilities, of which one must occur and s are regarded as successes, then the probability of success is s n . Example: Frequency interpretation of Probability: The probability of an event E is the proportion of times the event occurs during a long run of repeated experiments. Example: Def: A set function assigns a non-negative value to a set. Ex: N (A) is a set function whose value is the number of elements in A. Def: An additive set function f is a function for which f (A U B) = f (A) + f (B) when A and B are mutually exclusive. N (A) is an additive set function. Ex: Toss 2 fair dice. Let A be the event that the sum on the two dice is 5. Let B be the event that the sum on ...
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I've been in the field of "Cyber Security" in its many incarnations for about 25 years. In that time I've learned some lessons, some the hard way. Here are my slides presented at BSides New Orleans in April 2024.
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As privacy and data protection regulations evolve rapidly, organizations operating in multiple jurisdictions face mounting challenges to ensure compliance and safeguard customer data. With state-specific privacy laws coming up in multiple states this year, it is essential to understand what their unique data protection regulations will require clearly. How will data privacy evolve in the US in 2024? How to stay compliant? Our panellists will guide you through the intricacies of these states' specific data privacy laws, clarifying complex legal frameworks and compliance requirements. This webinar will review: - The essential aspects of each state's privacy landscape and the latest updates - Common compliance challenges faced by organizations operating in multiple states and best practices to achieve regulatory adherence - Valuable insights into potential changes to existing regulations and prepare your organization for the evolving landscape
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GenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdf
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Probability Fundas
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PROBABILITY FUNDAMENTALS PROF.
NAVEEN BHATIA
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PROBABILITY FUNDAMENTALS
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PROBABILITY FUNDAMENTALS 0
1 2 f(x) x
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PROBABILITY FUNDAS POSITIVE
SKEW NEGATIVE SKEW
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PROBABILITY FUNDAS f(x)
x λ EXPONENTIAL DENSITY FUNCTION
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PROPBABILITY FUNDAS 1
F(X) x DISTRIBUTION FUNCTION 1-e - λ x
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PROBABILITY FUNDAS φ
(x) z 0
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PROBABILITY FUNDAS
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