Conditionals predictions
... that determine the truth or falsehood of the antecedent: They are not yet “manifested” in the case of (1a), and “manifested” but unknown in the case of (1c). In Funk’s words, “the meaning of the conditioning frame can be said to vary from ‘if it happens that . . . ’ to ‘if it is true that . . . ’ ” ...
... that determine the truth or falsehood of the antecedent: They are not yet “manifested” in the case of (1a), and “manifested” but unknown in the case of (1c). In Funk’s words, “the meaning of the conditioning frame can be said to vary from ‘if it happens that . . . ’ to ‘if it is true that . . . ’ ” ...
On The Learnability Of Discrete Distributions
... Theorem 1 For any k 3, there is a xed sequence of fan-in k OR-gate circuits C1 ; : : : ; Cn ; : : : such that it is #P hard to determine for a given ~y 2 f0; 1gn the probability that ~y is generated by Cn . In other words, ORkn does not have polynomial-size evaluators, unless #P P=poly . Proof: ...
... Theorem 1 For any k 3, there is a xed sequence of fan-in k OR-gate circuits C1 ; : : : ; Cn ; : : : such that it is #P hard to determine for a given ~y 2 f0; 1gn the probability that ~y is generated by Cn . In other words, ORkn does not have polynomial-size evaluators, unless #P P=poly . Proof: ...
Dictatorships, Juntas, and Monomials
... value of f ′ (z). If f ′ is ǫ-close to f , then this vote equals the value f ′ (z) with probability at least Prr [f ′ (r) = f (r) & f ′ (r ⊕ z) = f (r ⊕ z)] ≥ 1 − 2ǫ, since f ′ (z) = f ′ (r) ⊕ f ′ (r ⊕ z) (by linearity of f ′ ). This discussion leads to a natural tester for monotone dictatorship, wh ...
... value of f ′ (z). If f ′ is ǫ-close to f , then this vote equals the value f ′ (z) with probability at least Prr [f ′ (r) = f (r) & f ′ (r ⊕ z) = f (r ⊕ z)] ≥ 1 − 2ǫ, since f ′ (z) = f ′ (r) ⊕ f ′ (r ⊕ z) (by linearity of f ′ ). This discussion leads to a natural tester for monotone dictatorship, wh ...
From: Jehle, G. and P. Reny, Advanced Microeconomic Theory, 2nd
... to each outcome in A. This warrants a bit of discussion. For example, suppose that A = {a1 , a2 }. Consider the compound gamble yielding outcome a1 with probability α, and yielding a lottery ticket with probability 1 − α, where the lottery ticket itself is a simple gamble. It yields the outcome a1 w ...
... to each outcome in A. This warrants a bit of discussion. For example, suppose that A = {a1 , a2 }. Consider the compound gamble yielding outcome a1 with probability α, and yielding a lottery ticket with probability 1 − α, where the lottery ticket itself is a simple gamble. It yields the outcome a1 w ...
Pre-Calculus • Unit 8
... MGSE9-12.S.MD.1 Define a random variable for a quantity of interest by assigning a numerical value to each event in a sample space; graph the corresponding probability distribution using the same graphical displays as for data distributions. MGSE9-12.S.MD.2 Calculate the expected value of a random v ...
... MGSE9-12.S.MD.1 Define a random variable for a quantity of interest by assigning a numerical value to each event in a sample space; graph the corresponding probability distribution using the same graphical displays as for data distributions. MGSE9-12.S.MD.2 Calculate the expected value of a random v ...
Basic Business Statistics, 10/e
... • 72.2% of the observations are within 1 standard deviation of the mean. (In a normal distribution this percentage is 68.26%. • 87% of the observations are within 1.28 standard deviations of the mean. (In a normal distribution percentage is 80%.) ...
... • 72.2% of the observations are within 1 standard deviation of the mean. (In a normal distribution this percentage is 68.26%. • 87% of the observations are within 1.28 standard deviations of the mean. (In a normal distribution percentage is 80%.) ...
Phylogenetic Reconstruction with Insertions and Deletions Alexandr Andoni , Mark Braverman
... B blocks, so it is enough to remember that B is a large constant times log n (the number of blocks does not have to be equal to the length of each block in order for the algorithm to succeed). We will also have bad blocks (which will also be called red blocks), and we will later prove that with high ...
... B blocks, so it is enough to remember that B is a large constant times log n (the number of blocks does not have to be equal to the length of each block in order for the algorithm to succeed). We will also have bad blocks (which will also be called red blocks), and we will later prove that with high ...
QMB 3250 - UF-Stat - University of Florida
... Note that measures such as per capita income are means. To obtain it, the total income for a region is obtained and divided by the number of people in the region. The mean represents what each individual would receive if the total for that variable were evenly split by all individuals. Median: Middl ...
... Note that measures such as per capita income are means. To obtain it, the total income for a region is obtained and divided by the number of people in the region. The mean represents what each individual would receive if the total for that variable were evenly split by all individuals. Median: Middl ...
Lecture 4
... about the data, other methods exist for analyzing the data which if the assumptions are valid is more “powerful” than the Bonferroni procedure. By more powerful we mean that they have a higher chance of rejecting the null hypothesis when it is false. ...
... about the data, other methods exist for analyzing the data which if the assumptions are valid is more “powerful” than the Bonferroni procedure. By more powerful we mean that they have a higher chance of rejecting the null hypothesis when it is false. ...
