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Books on the topic 'Gaussian Regression Processes'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Taeryon, Choi, ed. Gaussian process regression analysis for functional data. Boca Raton, FL: CRC Press, 2011.

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2

Rasmussen, Carl Edward. Evaluation of Gaussian processes and other methods for non-linear regression. Toronto: University of Toronto, Dept. of Computer Science, 1997.

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3

Neal, Radford M. Monte Carlo implementation of Gaussian process models for Bayesian regression and classification. Toronto: University of Toronto, 1997.

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4

Applied parameter estimation for chemical engineers. New York: Marcel Dekker, 2001.

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5

Shi, Jian Qing, and Taeryon Choi. Gaussian Process Regression Analysis for Functional Data. Taylor & Francis Group, 2011.

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6

Shi, Jian Qing, and Taeryon Choi. Gaussian Process Regression Analysis for Functional Data. Taylor & Francis Group, 2011.

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7

Shi, Jian Qing, and Taeryon Choi. Gaussian Process Regression Analysis for Functional Data. Taylor & Francis Group, 2011.

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8

Faraway, Julian J., Xiaofeng Wang, and Yu Ryan Yue. Bayesian Regression Modeling with INLA. Taylor & Francis Group, 2018.

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9

Faraway, Julian J., Xiaofeng Wang, and Yu Ryan Yue. Bayesian Regression Modeling with INLA. Taylor & Francis Group, 2018.

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10

Bayesian Regression Modeling with INLA. Taylor & Francis Group, 2018.

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11

Faraway, Julian J., Xiaofeng Wang, and Yu Yue Ryan. Bayesian Regression Modeling with INLA. Taylor & Francis Group, 2018.

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12

Faraway, Julian James, Yu Yue, and Xiaofeng Wang. Bayesian Regression Modeling with Inla. Taylor & Francis Group, 2020.

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13

Faraway, Julian J., Xiaofeng Wang, and Yu Ryan Yue. Bayesian Regression Modeling with INLA. Taylor & Francis Group, 2018.

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14

Bayesian Regression Modeling with INLA. Taylor & Francis Group, 2018.

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15

Lee, Herbert K. H., Matthew Taddy, Robert Gramacy, and Genetha Gray. Designing and analysing a circuit device experiment using treed Gaussian processes. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.28.

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Abstract:

This article describes a new circuit device, developed in collaboration with scientists at Sandia National Laboratories, based on treed Gaussian processes (TGP). The circuit devices under study are bipolar junction transistors, which are used to amplify electrical current. To aid with the design of the device, a computer model predicts its peak output as a function of the input dosage and a number of design parameters. The methodology also involves a novel sequential design procedure to generate data to fit the emulator. Both physical and computer simulation experiments are performed, and the results show that the TGP model can be useful for spatial data and semiparametric regression in the context of a computer experiment for designing a circuit device, for sequential design of (computer) experiments, sequential robust local optimization, validation, calibration, and sensitivity analysis.

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16

Englezos, Peter, and Nicolas Kalogerakis. Applied Parameter Estimation for Chemical Engineers. Taylor & Francis Group, 2019.

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17

Englezos, Peter, and Nicolas Kalogerakis. Applied Parameter Estimation for Chemical Engineers. Taylor & Francis Group, 2000.

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18

Englezos, Peter, and Nicolas Kalogerakis. Applied Parameter Estimation for Chemical Engineers. Taylor & Francis Group, 2000.

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19

Englezos, Peter, and Nicolas Kalogerakis. Applied Parameter Estimation for Chemical Engineers. Taylor & Francis Group, 2000.

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20

Englezos, Peter, and Nicolas Kalogerakis. Applied Parameter Estimation for Chemical Engineers. Taylor & Francis Group, 2000.

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21

Englezos, Peter, and Nicolas Kalogerakis. Applied Parameter Estimation for Chemical Engineers. Taylor & Francis Group, 2000.

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