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In R v Burgess [1991] 2 QB 92 the Court of Appeal ruled that the defendant, who wounded a woman by hitting her with a video recorder while sleepwalking, was insane under the M'Naghten Rules. Lord Lane said, "We accept that sleep is a normal condition, but the evidence in the instant case indicates that sleepwalking, and particularly violence in ...
In computer science, Thompson's construction algorithm, also called the McNaughton–Yamada–Thompson algorithm, [1] is a method of transforming a regular expression into an equivalent nondeterministic finite automaton (NFA). [2] This NFA can be used to match strings against the regular expression. This algorithm is credited to Ken Thompson.
dc: "Desktop Calculator" arbitrary-precision RPN calculator that comes standard on most Unix-like systems. KCalc, Linux based scientific calculator; Maxima: a computer algebra system which bignum integers are directly inherited from its implementation language Common Lisp. In addition, it supports arbitrary-precision floating-point numbers ...
Following the patent and release of Harold's Long Scale calculator featuring two knobs on the outside rim in 1914, he designed the Magnum Long Scale calculator in 1927. [ 6 ] [ 7 ] As the name "Magnum" implies, it was a fairly large device at 4.5 inches in diameter—about 1.5 inches more than Fowler's average non-Magnum-series calculators. [ 8 ]
Now we prove two lemmas about the above two concepts. Lemma 1 For any time k, among the times i,j < k such that w(0,i) and w(0,j) ∈ αβ*, the number of equivalence classes induced by E(i,j,k) is bounded by p. Also the number of equivalence classes induced by E(i,j) is bounded by p.
Assume that the combined system determined by two random variables and has joint entropy (,), that is, we need (,) bits of information on average to describe its exact state. Now if we first learn the value of X {\displaystyle X} , we have gained H ( X ) {\displaystyle \mathrm {H} (X)} bits of information.
In probability theory and information theory, the mutual information (MI) of two random variables is a measure of the mutual dependence between the two variables. More specifically, it quantifies the " amount of information " (in units such as shannons ( bits ), nats or hartleys ) obtained about one random variable by observing the other random ...
Higher values indicate higher predictability of the dependent variable from the independent variables, with a value of 1 indicating that the predictions are exactly correct and a value of 0 indicating that no linear combination of the independent variables is a better predictor than is the fixed mean of the dependent variable. [2]