Log in

Relevant bibliographies by topics / Bayesian Stochastic Optimization Model / Books

To see the other types of publications on this topic, follow the link: Bayesian Stochastic Optimization Model.

Books on the topic 'Bayesian Stochastic Optimization Model'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 September 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 15 books for your research on the topic 'Bayesian Stochastic Optimization Model.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse books on a wide variety of disciplines and organise your bibliography correctly.

1

Hakala, Tuula. A stochastic optimization model for multi-currency bond portfolio management. Helsinki School of Economics and Business Administration, 1996.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

2

Mischenko, Aleksandr, and Anastasiya Ivanova. Optimization models for managing limited resources in logistics. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1082948.

Full text

Abstract:

In the proposed monograph, optimization models for managing limited resources in logical systems are considered. Such systems are primarily used by industrial enterprises, transport companies and trade organizations, including those that carry out wholesale activities. As a rule, the efficiency of these objects largely depends on how rational use of limited resources such as: consumer camera business, labor, vehicles, etc. In this paper, various approaches to managing such resources are considered both for deterministic models and for the situation when a number of model parameters are not spe

APA, Harvard, Vancouver, ISO, and other styles

3

Cumming, Jonathan A., and Michael Goldstein. Bayesian analysis and decisions in nuclear power plant maintenance. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.9.

Full text

Abstract:

This article discusses the results of a study in Bayesian analysis and decision making in the maintenance and reliability of nuclear power plants. It demonstrates the use of Bayesian parametric and semiparametric methodology to analyse the failure times of components that belong to an auxiliary feedwater system in a nuclear power plant at the South Texas Project (STP) Electric Generation Station. The parametric models produce estimates of the hazard functions that are compared to the output from a mixture of Polya trees model. The statistical output is used as the most critical input in a stoc

APA, Harvard, Vancouver, ISO, and other styles

4

Lin, Ruitao, Ying Yuan, and J. Jack Lee. Model-Assisted Bayesian Designs for Dose Finding and Optimization: Methods and Applications. Taylor & Francis Group, 2022.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

5

Lin, Ruitao, Ying Yuan, and J. Jack Lee. Model-Assisted Bayesian Designs for Dose Finding and Optimization: Methods and Applications. Taylor & Francis Group, 2022.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

6

Lin, Ruitao, Ying Yuan, and J. Jack Lee. Model-Assisted Bayesian Designs for Dose Finding and Optimization: Methods and Applications. Taylor & Francis Group, 2022.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

7

Lin, Ruitao, Ying Yuan, and J. Jack Lee. Model-Assisted Bayesian Designs for Dose Finding and Optimization: Methods and Applications. Taylor & Francis Group, 2022.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

8

Lin, Ruitao, Ying Yuan, and J. Jack Lee. Model-Assisted Bayesian Designs for Dose Finding and Optimization: Methods and Applications. Taylor & Francis Group, 2022.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

9

Lin, Ruitao, Ying Yuan, and J. Jack Lee. Model-Assisted Bayesian Designs for Dose Finding and Optimization: Methods and Applications. Taylor & Francis Group, 2022.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

10

Mayer, Janos. Stochastic Linear Programming Algorithms: A Comparison Based on a Model Management System (Optimization Theory & Applications Series). CRC, 1998.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

11

Henderson, Daniel A., R. J. Boys, Carole J. Proctor, and Darren J. Wilkinson. Linking systems biology models to data: A stochastic kinetic model of p53 oscillations. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.7.

Full text

Abstract:

This article discusses the use of a stochastic kinetic model to study protein level oscillations in single living cancer cells, using the p53 and Mdm2 proteins as examples. It describes the refinement of a dynamic stochastic process model of the cellular response to DNA damage and compares this model to time course data on the levels of p53 and Mdm2. The article first provides a biological background on p53 and Mdm2 before explaining how the stochastic kinetic model is constructed. It then introduces the stochastic kinetic model and links it to the data and goes on to apply sophisticated MCMC

APA, Harvard, Vancouver, ISO, and other styles

12

Juillard, Michel. Dynamic Stochastic General Equilibrium Models. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.4.

Full text

Abstract:

Dynamic Stochastic General Equilibrium (DSGE) models have become popular in macroeconomics, but the combination of nonlinear microeconomic behavior of the agents and model-consistent expectations raise intricate computational issues; this chapter reviews solution methods and estimation of DSGE models. Perfect foresight deterministic models can easily be solved with a great degree of accuracy. In practice, medium-sized stochastic models can only be solved by local approximation or the perturbation approach. The Bayesian approach to estimation is privileged. It provides a convenient way to commu

APA, Harvard, Vancouver, ISO, and other styles

13

Challenor, Peter, Doug McNeall, and James Gattiker. The new macroeconometrics: A Bayesian approach. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.15.

Full text

Abstract:

This article examines the dynamics of the US economy over the last five decades using Bayesian analysis of dynamic stochastic general equilibrium (DSGE) models. It highlights an example application in what is commonly referred to as the new macroeconometrics, which combines macroeconomics with econometrics. The article describes a benchmark New Keynesian DSGE model that incorporates four types of agents: households that consume, save, and supply labour to a labour ‘packer’; a labour ‘packer’ that puts together the labour supplied by different households into an homogeneous labour unit; interme

APA, Harvard, Vancouver, ISO, and other styles

14

Quintana, José Mario, Carlos Carvalho, James Scott, and Thomas Costigliola. Extracting S&P500 and NASDAQ Volatility: The Credit Crisis of 2007–2008. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.13.

Full text

Abstract:

This article demonstrates the utility of Bayesian modelling and inference in financial market volatility analysis, using the 2007-2008 credit crisis as a case study. It first describes the applied problem and goal of the Bayesian analysis before introducing the sequential estimation models. It then discusses the simulation-based methodology for inference, including Markov chain Monte Carlo (MCMC) and particle filtering methods for filtering and parameter learning. In the study, Bayesian sequential model choice techniques are used to estimate volatility and volatility dynamics for daily data fo

APA, Harvard, Vancouver, ISO, and other styles

15

Tuite, Cl´ıodhna, Michael O’Neill, and Anthony Brabazon. Economic and Financial Modeling with Genetic Programming. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.10.

Full text

Abstract:

This chapter focuses on genetic programming (GP), a stochastic optimization and model induction technique. An advantage of GP is that the modeler need not select the exact parameters to be used in the model beforehand. Rather, GP can effectively search a complex model space defined by a set of building blocks specified by the modeler. This flexibility has allowed GP to be used for many applications. The chapter reviews some of the most significant developments using GP: forecasting, stock selection, derivative pricing and trading, bankruptcy and credit risk assessment, and agent-based and econ

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!