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  2. Wilks' theorem - Wikipedia

    en.wikipedia.org/wiki/Wilks'_theorem

    For example: If the null model has 1 parameter and a log-likelihood of −8024 and the alternative model has 3 parameters and a log-likelihood of −8012, then the probability of this difference is that of chi-squared value of (()) = with = degrees of freedom, and is equal to .

  3. Probability density function - Wikipedia

    en.wikipedia.org/wiki/Probability_density_function

    In this example, the ratio (probability of living during an interval) / (duration of the interval) is approximately constant, and equal to 2 per hour (or 2 hour −1). For example, there is 0.02 probability of dying in the 0.01-hour interval between 5 and 5.01 hours, and (0.02 probability / 0.01 hours) = 2 hour −1.

  4. De Morgan's laws - Wikipedia

    en.wikipedia.org/wiki/De_Morgan's_laws

    De Morgan's laws represented with Venn diagrams.In each case, the resultant set is the set of all points in any shade of blue. In propositional logic and Boolean algebra, De Morgan's laws, [1] [2] [3] also known as De Morgan's theorem, [4] are a pair of transformation rules that are both valid rules of inference.

  5. Boolean satisfiability problem - Wikipedia

    en.wikipedia.org/wiki/Boolean_satisfiability_problem

    For example, the formula "a AND NOT b" is satisfiable because one can find the values a = TRUE and b = FALSE, which make (a AND NOT b) = TRUE. In contrast, "a AND NOT a" is unsatisfiable. SAT is the first problem that was proven to be NP-complete—this is the Cook–Levin theorem.

  6. Normal distribution - Wikipedia

    en.wikipedia.org/wiki/Normal_distribution

    In probability theory and statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable.The general form of its probability density function is [2] [3] = ().

  7. Probability distribution - Wikipedia

    en.wikipedia.org/wiki/Probability_distribution

    Probability density function (pdf) or probability density: function whose value at any given sample (or point) in the sample space (the set of possible values taken by the random variable) can be interpreted as providing a relative likelihood that the value of the random variable would equal that sample.

  8. Conditional event algebra - Wikipedia

    en.wikipedia.org/wiki/Conditional_event_algebra

    In standard practice, P(if A, then B) is not interpreted as P(A′ ∪ B), following the rule of material implication, but rather as the conditional probability of B given A, P(B | A) = P(A ∩ B) / P(A). This raises a question: what about a probability like P(if A, then B, and if C, then D)? For this, there is no standard answer.

  9. Binomial proportion confidence interval - Wikipedia

    en.wikipedia.org/wiki/Binomial_proportion...

    The probability density function (PDF) for the Wilson score interval, plus PDF s at interval bounds. Tail areas are equal. Since the interval is derived by solving from the normal approximation to the binomial, the Wilson score interval ( , + ) has the property of being guaranteed to obtain the same result as the equivalent z-test or chi-squared test.