The Experts below are selected from a list of 1005 Experts worldwide ranked by ideXlab platform
Maria D Bovea - One of the best experts on this subject based on the ideXlab platform.
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modelling energy efficiency performance of residential building stocks based on Bayesian Statistical Inference
Environmental Modelling and Software, 2016Co-Authors: Marta Brauliogonzalo, Pablo Juan, Maria D BoveaAbstract: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 - One of the best experts on this subject based on the ideXlab platform.
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Bayesian approach to a definition of random sequences and its applications to Statistical Inference
International Symposium on Information Theory, 2006Co-Authors: H TakahashiAbstract: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.
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ISIT - Bayesian approach to a definition of random sequences and its applications to Statistical Inference
2006 IEEE International Symposium on Information Theory, 2006Co-Authors: H TakahashiAbstract: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 - One of the best experts on this subject based on the ideXlab platform.
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modelling energy efficiency performance of residential building stocks based on Bayesian Statistical Inference
Environmental Modelling and Software, 2016Co-Authors: Marta Brauliogonzalo, Pablo Juan, Maria D BoveaAbstract: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.
Changsheng Zhang - One of the best experts on this subject based on the ideXlab platform.
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Bayesian Statistical Inference based estimation of distribution algorithm for the re entrant job shop scheduling problem with sequence dependent setup times
International Conference on Intelligent Computing, 2014Co-Authors: Shaofeng Chen, Bin Qian, Rong Hu, Changsheng ZhangAbstract:In this paper, a Bayesian Statistical Inference-based estimation of distribution algorithm (BEDA) is proposed for the re-entrant job-shop scheduling problem with sequence-dependent setup times (RJSSPST) to minimize the maximum completion time (i.e., makespan), which is a typical NP hard combinatorial problem with strong engineering background. Bayesian Statistical Inference (BSI) is utilized to extract sub-sequence information from high quality individuals of the current population and determine the parameters of BEDA’s probabilistic model (BEDA_PM). In the proposed BEDA, BEDA_PM is used to generate new population and guide the search to find promising sequences or regions in the solution space. Simulation experiments and comparisons demonstrate the effectiveness of the proposed BEDA.
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ICIC (2) - Bayesian Statistical Inference-Based Estimation of Distribution Algorithm for the Re-entrant Job-Shop Scheduling Problem with Sequence-Dependent Setup Times
Intelligent Computing Methodologies, 2014Co-Authors: Shaofeng Chen, Bin Qian, Rong Hu, Changsheng ZhangAbstract:In this paper, a Bayesian Statistical Inference-based estimation of distribution algorithm (BEDA) is proposed for the re-entrant job-shop scheduling problem with sequence-dependent setup times (RJSSPST) to minimize the maximum completion time (i.e., makespan), which is a typical NP hard combinatorial problem with strong engineering background. Bayesian Statistical Inference (BSI) is utilized to extract sub-sequence information from high quality individuals of the current population and determine the parameters of BEDA’s probabilistic model (BEDA_PM). In the proposed BEDA, BEDA_PM is used to generate new population and guide the search to find promising sequences or regions in the solution space. Simulation experiments and comparisons demonstrate the effectiveness of the proposed BEDA.
Janwillem Romeijn - One of the best experts on this subject based on the ideXlab platform.
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Logical relations in a Statistical problem
2020Co-Authors: Janwillem Romeijn, Rolf Haenni, Gregory Wheeler, Jon WilliamsonAbstract:This paper presents the progicnet programme. It proposes a general framework for probabilistic logic that can guide Inference based on both logical and probabilistic input, and it introduces a common calculus for making Inferences in the framework. After an introduction to the programme as such, it is illustrated by means of a toy example from psychometrics. It is shown that the framework and calculus can accommodate a number of approaches to probabilistic reasoning: Bayesian Statistical Inference, evidential probability, probabilistic argumentation, and objective Bayesianism. The progicnet programme thus provides insight into the relations between these approaches, it illustrates how the results of different approaches can be combined, and it provides a basis for doing efficient Inference in each
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networks for Bayesian Statistical Inference
2011Co-Authors: Rolf Haenni, Janwillem Romeijn, Gregory Wheeler, Jon WilliamsonAbstract:We first spell out how a credal network can be related to a Statistical model, i.e. a set of Statistical hypotheses. Recall that a credal network is associated with a credal set, a set of probability functions over some designated set of variables. Hence a credal set may be viewed as a Statistical model: each element of the credal set is a probability function over the set of variables, and this probability may be read as a likelihood of some hypothesis for observations of valuations of the network. Conversely, any Statistical model concerns inter-related trials of some specific set of variables, so that we can identify any Statistical model with a credal network containing these variables. Here we deal with non-causal Statistical hypotheses; (Leuridan, 2008, Chapter 4) argues that credal nets can also be used to represent causal hypotheses. An detailed illustration of many ideas in this section can be found in (Romeijn et al., 2009).
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Bayesian Statistical Inference
2011Co-Authors: Rolf Haenni, Janwillem Romeijn, Gregory Wheeler, Jon WilliamsonAbstract:Bayesian statistics is much more easily connected to the inferential problem of Schema (1.1) than classical statistics. The feature that distinguishes Bayesian Statistical Inference from classical statistics is that it also employs probability assignments over Statistical hypotheses. It is therefore possible to present a Bayesian Statistical procedure as an Inference concerning probability assignments over hypotheses. Recall that we called the Inference of probability assignments over data on the assumption of a Statistical hypothesis direct. Because in Bayesian Inference we derive a probability assignment over hypotheses on the basis of data, it is sometimes called indirect Inference.
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theory change and Bayesian Statistical Inference
Philosophy of Science, 2005Co-Authors: Janwillem RomeijnAbstract:This paper addresses the problem that Bayesian Statistical Inference cannot accommodate theory change, and proposes a framework for dealing with such changes. It first presents a scheme for generating predictions from observations by means of hypotheses. An example shows how the hypotheses represent the theoretical structure underlying the scheme. This is followed by an example of a change of hypotheses. The paper then presents a general framework for hypotheses change, and proposes the minimization of the distance between hypotheses as a rationality criterion. Finally the paper discusses the import of this for Bayesian Statistical Inference.
