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On the first scale, the experts performed worse than they would have if they had simply assigned an equal probability to all three outcomes — if they had given each possible future a thirty-three-per-cent chance of occurring.
At each point of decision, the UE randomly selects the next moving direction with equal probability to all possible destinations.
Wlodeck Rabinovich (1992) takes this idea even further and argues that from the principle of indifference, players must assign equal probability to all choice-worthy options.
First, we run the BCJR algorithm where the branch metrics are initialized without any initial channel guess by assigning zero a priori probability to all transitions that are not consistent with the known pilots and equal (nonzero) a priori probability to all transitions that are consistent with the pilots.
Kalai and Lehrer prove that their procedure almost always converges to something approximating Nash equilibrium, under the crucial assumption (the "grain of truth" assumption) that each player begins by assigning positive probability to all strategies that actually occur.
As prior distribution, Fasbender et al.[73] assumed a noninformative prior p z) ∝ 1, which gives equal probability to all possible solutions, that is, no solution is preferred as no clear information on the HRMS image is available.
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A prior distribution for a Bayesian model that assigns equal probabilities to all models.
It follows that the mathematical model for choosing a number at random from [0, 1] and that of tossing a fair coin infinitely many times assign the same probabilities to all intervals of the form [k/2n, 1/2n].
This is based on the principle of indifference, which assigns equal probabilities to all possibilities[29].
(2) For an arbitrary node, the transmission probabilities to all neighboring nodes are equal.
At the initial phase, MPI assigns identical initial probabilities to all unknown objects based on Principle of Indifference [30].
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