random experiment वाक्य
उदाहरण वाक्य
मोबाइल
- In this example the random experiment consists of flipping three fair coins.
- Stanislaw Ulam had the idea of using random experiments.
- She therefore conceives of the following random experiment.
- That's because the only two remaining outcomes for this random experiment are:
- To apply the method of conditional probabilities, first model the random experiment as a sequence of small random steps.
- But in a random experiment, the probability of being horizontally polarized is, and the probability of being vertically polarized is.
- It is of course impossible to actually perform an infinity of repetitions of a random experiment to determine the probability of an event.
- But some important random experiments, such as tossing a coin until it rises heads, give rise to an infinite set of outcomes.
- With this mode of convergence, we increasingly expect to see the next outcome in a sequence of random experiments becoming better and better modeled by a given probability distribution.
- Rather than actually carry out, and possibly have to repeat, the rather long random experiment, the protagonist decides to simply calculate and use the most probable result.
- A trial of the random experiment corresponds to taking a random walk from the root ( the top node in the tree, where no coins have been flipped ) to a leaf.
- To apply the method to a probabilistic proof, the randomly chosen object in the proof must be choosable by a random experiment that consists of a sequence of " small " random choices.
- The basic idea is to replace each random choice in a random experiment by a deterministic choice, so as to keep the conditional probability of failure, given the choices so far, below 1.
- The method of conditional probabilities replaces the random root-to-leaf walk in the random experiment by a deterministic root-to-leaf walk, where each step is chosen to inductively maintain the following invariant:
- For two elements x, y \ in \ mathcal X the random variables Z ( x ) and Z ( y ) will not be uncorrelated, because if x is too close to y the random experiments described by Z ( x ) and Z ( y ) will often show similar behaviour.
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