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In probability theory, the rule of succession is a formula introduced in the 18th century by Pierre-Simon Laplace in the course of treating the sunrise problem. [1] The formula is still used, particularly to estimate underlying probabilities when there are few observations or events that have not been observed to occur at all in (finite) sample data.
In particular, if a beam of spin-oriented spin- 1 / 2 particles is split, and just one of the beams is rotated about the axis of its direction of motion and then recombined with the original beam, different interference effects are observed depending on the angle of rotation. In the case of rotation by 360°, cancellation effects are ...
One example of Rabi flopping is the spin flipping within a quantum system containing a spin-1/2 particle and an oscillating magnetic field. We split the magnetic field into a constant 'environment' field, and the oscillating part, so that our field looks like = + = + ( + ()) where and are the strengths of the environment and the oscillating fields respectively, and is the frequency at ...
The composition of two rotations is itself a rotation. Let (a 1, b 1, c 1, d 1) and (a 2, b 2, c 2, d 2) be the Euler parameters of two rotations. The parameters for the compound rotation (rotation 2 after rotation 1) are as follows:
In three dimensions the cross product is invariant under the action of the rotation group, SO(3), so the cross product of x and y after they are rotated is the image of x × y under the rotation. But this invariance is not true in seven dimensions; that is, the cross product is not invariant under the group of rotations in seven dimensions, SO(7).
This is called the addition law of probability, or the sum rule. That is, the probability that an event in A or B will happen is the sum of the probability of an event in A and the probability of an event in B, minus the probability of an event that is in both A and B. The proof of this is as follows: Firstly,
Symbolically, the rotation invariance of a real-valued function of two real variables is f ( x ′ ) = f ( R x ) = f ( x ) {\displaystyle f(\mathbf {x} ')=f(\mathbf {Rx} )=f(\mathbf {x} )} In words, the function of the rotated coordinates takes exactly the same form as it did with the initial coordinates, the only difference is the rotated ...
If the original random variable X is uniformly distributed on the interval (a,b), where a>0, then the reciprocal variable Y = 1 / X has the reciprocal distribution which takes values in the range (b −1,a −1), and the probability density function in this range is =, and is zero elsewhere.