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Murray D. Smith - One of the best experts on this subject based on the ideXlab platform.

  • Symbolic Maximum Likelihood Estimation with Mathematica
    Journal of the Royal Statistical Society: Series D (The Statistician), 2000
    Co-Authors: Colin Rose, Murray D. Smith
    Abstract:

    Mathematica is a symbolic programming language that empowers the user to undertake complicated algebraic tasks. One such task is the derivation of Maximum Likelihood estimators, demonstrably an important topic in statistics at both the research and expository level. In this paper, a Mathematica package is provided that contains a function entitled SuperLog. This function utilises pattern-matching code that enhances Mathematica's ability to simplify expressions involving the natural logarithm of a product of algebraic terms. This enhancement to Mathematica's functionality can be of particular benefit for Maximum Likelihood Estimation.

Jaromír Fiurášek - One of the best experts on this subject based on the ideXlab platform.

  • Maximum-Likelihood Estimation of quantum measurement
    Physical Review A, 2001
    Co-Authors: Jaromír Fiurášek
    Abstract:

    Maximum-Likelihood Estimation is applied to the determination of an unknown quantum measurement. The calibrated measuring apparatus carries out measurements on many different quantum states and the positive operator-valued measure governing the measurement statistics is then inferred from the collected data via the Maximum-Likelihood principle. In contrast to the procedures based on linear inversion, our approach always provides a physically sensible result. We illustrate the method on the case of the Stern-Gerlach apparatus.

  • Maximum-Likelihood Estimation of quantum processes
    2001
    Co-Authors: Jaromír Fiurášek, Zdenek Hradil
    Abstract:

    Maximum-Likelihood Estimation is applied to identification of an unknown quantum mechanical process. In contrast to linear reconstruction schemes the proposed approach always yields physically sensible results. Its feasibility is demonstrated by means of Monte Carlo simulations for the two-level system (single qubit).

  • Maximum-Likelihood Estimation of quantum processes
    Physical Review A, 2001
    Co-Authors: Jaromír Fiurášek, Zdenek Hradil
    Abstract:

    Maximum-Likelihood Estimation is applied to identification of an unknown quantum-mechanical process. In contrast to linear reconstruction schemes, the proposed approach always yields physically sensible results. Its feasibility is demonstrated by performing the Monte Carlo simulations for the two-level system (single qubit).

Colin Rose - One of the best experts on this subject based on the ideXlab platform.

  • Symbolic Maximum Likelihood Estimation with Mathematica
    Journal of the Royal Statistical Society: Series D (The Statistician), 2000
    Co-Authors: Colin Rose, Murray D. Smith
    Abstract:

    Mathematica is a symbolic programming language that empowers the user to undertake complicated algebraic tasks. One such task is the derivation of Maximum Likelihood estimators, demonstrably an important topic in statistics at both the research and expository level. In this paper, a Mathematica package is provided that contains a function entitled SuperLog. This function utilises pattern-matching code that enhances Mathematica's ability to simplify expressions involving the natural logarithm of a product of algebraic terms. This enhancement to Mathematica's functionality can be of particular benefit for Maximum Likelihood Estimation.

Zdenek Hradil - One of the best experts on this subject based on the ideXlab platform.

  • Maximum-Likelihood Estimation of quantum processes
    2001
    Co-Authors: Jaromír Fiurášek, Zdenek Hradil
    Abstract:

    Maximum-Likelihood Estimation is applied to identification of an unknown quantum mechanical process. In contrast to linear reconstruction schemes the proposed approach always yields physically sensible results. Its feasibility is demonstrated by means of Monte Carlo simulations for the two-level system (single qubit).

  • Maximum-Likelihood Estimation of quantum processes
    Physical Review A, 2001
    Co-Authors: Jaromír Fiurášek, Zdenek Hradil
    Abstract:

    Maximum-Likelihood Estimation is applied to identification of an unknown quantum-mechanical process. In contrast to linear reconstruction schemes, the proposed approach always yields physically sensible results. Its feasibility is demonstrated by performing the Monte Carlo simulations for the two-level system (single qubit).

Myungin Jae - One of the best experts on this subject based on the ideXlab platform.