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Bayesian Statistical Inference

The Experts below are selected from a list of 1005 Experts worldwide ranked by ideXlab platform

Maria D Bovea – 1st expert on this subject based on the ideXlab platform

  • modelling energy efficiency performance of residential building stocks based on Bayesian Statistical Inference
    Environmental Modelling and Software, 2016
    Co-Authors: Marta Brauliogonzalo, Pablo Juan, Maria D Bovea

    Abstract:

    This paper provides a model based on Integrated Nested Laplace Approximation to predict the energy performance of existing residential building stocks. The energy demand and the discomfort hours for heating and cooling were taken as response variables and five parameters were considered as potentially significant to assess the building energy performance: urban block pattern, street height-width ratio, building class through the building shape factor, year of construction and solar orientation of the main facade. A total of 240 dynamic energy simulations were run varying these parameters, by using the EnergyPlus software with the Design Builder interface, which allowed the response variables to be determined for a set of sample buildings. Simulation results revealed the most and least significant parameters in the energy performance of the buildings. The model developed is a useful decision-making tool in assisting local authorities during energy refurbishment interventions at the urban scale. A model to predict the energy performance of residential building stocks is developed.The energy demand and the discomfort hours were considered as output of the model.Bayesian Inference based on INLA was the Statistical framework of the model.The model is useful to identify urban areas that require urgent energy refurbishment.

H Takahashi – 2nd expert on this subject based on the ideXlab platform

  • Bayesian approach to a definition of random sequences and its applications to Statistical Inference
    International Symposium on Information Theory, 2006
    Co-Authors: H Takahashi

    Abstract:

    We introduce a universal Bayes test, which is a Bayesian version of Martin-Lof test. Then we define random sequences with respect to parametric models based on our universal Bayes test. We state some theorems related to Bayesian Statistical Inference in terms of random sequence.

  • ISIT – Bayesian approach to a definition of random sequences and its applications to Statistical Inference
    2006 IEEE International Symposium on Information Theory, 2006
    Co-Authors: H Takahashi

    Abstract:

    We introduce a universal Bayes test, which is a Bayesian version of Martin-Lof test. Then we define random sequences with respect to parametric models based on our universal Bayes test. We state some theorems related to Bayesian Statistical Inference in terms of random sequence.

Marta Brauliogonzalo – 3rd expert on this subject based on the ideXlab platform

  • modelling energy efficiency performance of residential building stocks based on Bayesian Statistical Inference
    Environmental Modelling and Software, 2016
    Co-Authors: Marta Brauliogonzalo, Pablo Juan, Maria D Bovea

    Abstract:

    This paper provides a model based on Integrated Nested Laplace Approximation to predict the energy performance of existing residential building stocks. The energy demand and the discomfort hours for heating and cooling were taken as response variables and five parameters were considered as potentially significant to assess the building energy performance: urban block pattern, street height-width ratio, building class through the building shape factor, year of construction and solar orientation of the main facade. A total of 240 dynamic energy simulations were run varying these parameters, by using the EnergyPlus software with the Design Builder interface, which allowed the response variables to be determined for a set of sample buildings. Simulation results revealed the most and least significant parameters in the energy performance of the buildings. The model developed is a useful decision-making tool in assisting local authorities during energy refurbishment interventions at the urban scale. A model to predict the energy performance of residential building stocks is developed.The energy demand and the discomfort hours were considered as output of the model.Bayesian Inference based on INLA was the Statistical framework of the model.The model is useful to identify urban areas that require urgent energy refurbishment.