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  2. Law of total expectation - Wikipedia

    en.wikipedia.org/wiki/Law_of_total_expectation

    The proposition in probability theory known as the law of total expectation, [1] the law of iterated expectations [2] (LIE), Adam's law, [3] the tower rule, [4] and the smoothing theorem, [5] among other names, states that if is a random variable whose expected value ⁡ is defined, and is any random variable on the same probability space, then

  3. Proofs involving ordinary least squares - Wikipedia

    en.wikipedia.org/wiki/Proofs_involving_ordinary...

    The normal equations can be derived directly from a matrix representation of the problem as follows. The objective is to minimize = ‖ ‖ = () = +.Here () = has the dimension 1x1 (the number of columns of ), so it is a scalar and equal to its own transpose, hence = and the quantity to minimize becomes

  4. Law of the iterated logarithm - Wikipedia

    en.wikipedia.org/wiki/Law_of_the_iterated_logarithm

    The law of iterated logarithms operates "in between" the law of large numbers and the central limit theorem.There are two versions of the law of large numbers — the weak and the strong — and they both state that the sums S n, scaled by n −1, converge to zero, respectively in probability and almost surely:

  5. Law of iterated expectations - Wikipedia

    en.wikipedia.org/?title=Law_of_iterated...

    Language links are at the top of the page across from the title.

  6. Law of total probability - Wikipedia

    en.wikipedia.org/wiki/Law_of_total_probability

    The term law of total probability is sometimes taken to mean the law of alternatives, which is a special case of the law of total probability applying to discrete random variables. [ citation needed ] One author uses the terminology of the "Rule of Average Conditional Probabilities", [ 4 ] while another refers to it as the "continuous law of ...

  7. Law of total variance - Wikipedia

    en.wikipedia.org/wiki/Law_of_total_variance

    In probability theory, the law of total variance [1] or variance decomposition formula or conditional variance formulas or law of iterated variances also known as Eve's law, [2] states that if and are random variables on the same probability space, and the variance of is finite, then

  8. Law of total covariance - Wikipedia

    en.wikipedia.org/wiki/Law_of_total_covariance

    The law of total covariance can be proved using the law of total expectation: First, ⁡ (,) = ⁡ [] ⁡ [] ⁡ [] from a simple standard identity on covariances. Then we apply the law of total expectation by conditioning on the random variable Z:

  9. Proofs and Refutations - Wikipedia

    en.wikipedia.org/wiki/Proofs_and_Refutations

    The 1976 book Proofs and Refutations is based on the first three chapters of his 1961 four-chapter doctoral thesis Essays in the Logic of Mathematical Discovery.But its first chapter is Lakatos's own revision of its chapter 1 that was first published as Proofs and Refutations in four parts in 1963–4 in the British Journal for the Philosophy of Science.