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Billingsley, Patrick. Statistical Methods in Markov Chains. Corrections to "Statistical Methods in Markow Chains". Volume 32, Number 4 (1961), 1343-1343.

Billingsley, Patrick. Project Euclid: euclid. The Institute of Mathematical Statistics.

Statistical Inference for Markov Processes (P. Billingsley). We study a quantum algorithm that consists of a simple quantum Markov process, and we analyze its behavior on restricted versions of Quantum 2-SAT. We prove that the algorithm solves this decision problem with high probability for n qubits, L clauses, and promise gap c in time O(n^2 L^2 c^{-2}). If the Hamiltonian is additionally polynomially gapped, our algorithm efficiently produces a state. that has high overlap with the satisfying subspace. The Markov process we study is a quantum analogue of Sch"oning's probabilistic algorithm for k-SAT.

Statistical inference is the process of using data analysis to deduce properties of an underlying probability distribution. Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates. It is assumed that the observed data set is sampled from a larger population. Inferential statistics can be contrasted with descriptive statistics

Billingsley, . Statistical inference for Markov processes.

Billingsley, . 2. Cartan, . Théorie élémentaire des fonctions analytiques d'une ou plusieurs variables complexes. Paris: Hermann 1961Google Scholar. 3. Chung, K. Markov chains with stationary transition probabilities. w York: Springer 1967Google Scholar.

Statistical Inference. by Patrick Billingsley. See a Problem? We’d love your help. Details (if other): Cancel. Thanks for telling us about the problem. by. Patrick Billingsley.

Semantic Scholar extracted view of "Statistical Inference for Markov . oceedings{alIF, title {Statistical Inference for Markov Processes. S. Keeping and Patrick Billingsley}, year {1961} }. E. Keeping, Patrick Billingsley

Semantic Scholar extracted view of "Statistical Inference for Markov Processes. Keeping, Patrick Billingsley.

By Patrick Billingsley. Vol. I: Pp. 127; 42s.

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Such processes are defined by a deterministic motion punctuated by random jumps at random times, and offer simple yet challenging models to study. Nevertheless, the issue of statistical estimation of the parameters ruling the jump mechanism is far from trivial.