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Paglia J, Eidsvik J, Karvanen J. Efficient spatial designs using Hausdorff distances and Bayesian optimization. Scand Stat Theory Appl 2021. [DOI: 10.1111/sjos.12554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Jacopo Paglia
- Department of Mathematical Sciences Norwegian University of Science and Technology Trondheim Norway
| | - Jo Eidsvik
- Department of Mathematical Sciences Norwegian University of Science and Technology Trondheim Norway
| | - Juha Karvanen
- Department of Mathematics and Statistics University of Jyvaskyla Jyväskylä Finland
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2
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Cooper M, McGree J, Molloy TL, Ford JJ. Bayesian Experimental Design With Application to Dynamical Vehicle Models. IEEE T ROBOT 2021. [DOI: 10.1109/tro.2021.3063977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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3
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Lomeli LM, Iniguez A, Tata P, Jena N, Liu ZY, Van Etten R, Lander AD, Shahbaba B, Lowengrub JS, Minin VN. Optimal experimental design for mathematical models of haematopoiesis. J R Soc Interface 2021; 18:20200729. [PMID: 33499768 PMCID: PMC7879761 DOI: 10.1098/rsif.2020.0729] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 01/04/2021] [Indexed: 11/12/2022] Open
Abstract
The haematopoietic system has a highly regulated and complex structure in which cells are organized to successfully create and maintain new blood cells. It is known that feedback regulation is crucial to tightly control this system, but the specific mechanisms by which control is exerted are not completely understood. In this work, we aim to uncover the underlying mechanisms in haematopoiesis by conducting perturbation experiments, where animal subjects are exposed to an external agent in order to observe the system response and evolution. We have developed a novel Bayesian hierarchical framework for optimal design of perturbation experiments and proper analysis of the data collected. We use a deterministic model that accounts for feedback and feedforward regulation on cell division rates and self-renewal probabilities. A significant obstacle is that the experimental data are not longitudinal, rather each data point corresponds to a different animal. We overcome this difficulty by modelling the unobserved cellular levels as latent variables. We then use principles of Bayesian experimental design to optimally distribute time points at which the haematopoietic cells are quantified. We evaluate our approach using synthetic and real experimental data and show that an optimal design can lead to better estimates of model parameters.
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Affiliation(s)
- Luis Martinez Lomeli
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA, USA
| | - Abdon Iniguez
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA, USA
| | - Prasanthi Tata
- Division of Hematology/Oncology, University of California Irvine, Irvine, CA, USA
| | - Nilamani Jena
- Division of Hematology/Oncology, University of California Irvine, Irvine, CA, USA
| | - Zhong-Ying Liu
- Division of Hematology/Oncology, University of California Irvine, Irvine, CA, USA
| | - Richard Van Etten
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA, USA
- Division of Hematology/Oncology, University of California Irvine, Irvine, CA, USA
- Department of Biological Chemistry, University of California Irvine, Irvine, CA, USA
- Center for Cancer Systems Biology, University of California Irvine, Irvine, CA, USA
- Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, CA, USA
| | - Arthur D. Lander
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA, USA
- Center for Cancer Systems Biology, University of California Irvine, Irvine, CA, USA
- Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, CA, USA
- Department of Developmental and Cell Biology, University of California Irvine, Irvine, CA, USA
- Department of Biomedical Engineering, University of California Irvine, Irvine, CA, USA
| | - Babak Shahbaba
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA, USA
- Center for Cancer Systems Biology, University of California Irvine, Irvine, CA, USA
- Department of Statistics, University of California Irvine, Irvine, CA, USA
| | - John S. Lowengrub
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA, USA
- Center for Cancer Systems Biology, University of California Irvine, Irvine, CA, USA
- Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, CA, USA
- Department of Biomedical Engineering, University of California Irvine, Irvine, CA, USA
- Department of Mathematics, University of California Irvine, Irvine, CA, USA
| | - Vladimir N. Minin
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA, USA
- Center for Cancer Systems Biology, University of California Irvine, Irvine, CA, USA
- Department of Statistics, University of California Irvine, Irvine, CA, USA
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4
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Bayesian sequential design for Copula models. TEST-SPAIN 2020. [DOI: 10.1007/s11749-019-00661-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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5
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Moffat H, Hainy M, Papanikolaou NE, Drovandi C. Sequential experimental design for predator-prey functional response experiments. J R Soc Interface 2020; 17:20200156. [PMID: 32396811 DOI: 10.1098/rsif.2020.0156] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Understanding functional response within a predator-prey dynamic is a cornerstone for many quantitative ecological studies. Over the past 60 years, the methodology for modelling functional response has gradually transitioned from the classic mechanistic models to more statistically oriented models. To obtain inferences on these statistical models, a substantial number of experiments need to be conducted. The obvious disadvantages of collecting this volume of data include cost, time and the sacrificing of animals. Therefore, optimally designed experiments are useful as they may reduce the total number of experimental runs required to attain the same statistical results. In this paper, we develop the first sequential experimental design method for predator-prey functional response experiments. To make inferences on the parameters in each of the statistical models we consider, we use sequential Monte Carlo, which is computationally efficient and facilitates convenient estimation of important utility functions. It provides coverage of experimental goals including parameter estimation, model discrimination as well as a combination of these. The results of our simulation study illustrate that for predator-prey functional response experiments sequential design outperforms static design for our experimental goals. R code for implementing the methodology is available via https://github.com/haydenmoffat/sequential_design_for_predator_prey_experiments.
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Affiliation(s)
- Hayden Moffat
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.,ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), Queensland University of Technology, Brisbane, Australia
| | - Markus Hainy
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.,Institute of Applied Statistics, Johannes Kepler University, Linz, Austria
| | - Nikos E Papanikolaou
- Directorate of Plant Protection, Greek Ministry of Rural Development and Food, Athens, Greece.,Laboratory of Agricultural Zoology and Entomology, Agricultural University of Athens, Greece.,Benaki Phytopathological Institute, Athens, Greece
| | - Christopher Drovandi
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.,ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), Queensland University of Technology, Brisbane, Australia
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6
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Affiliation(s)
- P. Richard Hahn
- Booth School of Business, The University of Chicago, Chicago, IL
| | - Ryan Martin
- Department of Statistics, North Carolina State University, NC
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7
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Jones M, Goldstein M, Jonathan P, Randell D. Bayes linear analysis of risks in sequential optimal design problems. Electron J Stat 2018. [DOI: 10.1214/18-ejs1496] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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8
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McGree J. Developments of the total entropy utility function for the dual purpose of model discrimination and parameter estimation in Bayesian design. Comput Stat Data Anal 2017. [DOI: 10.1016/j.csda.2016.05.020] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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9
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Drovandi CC, Holmes C, McGree JM, Mengersen K, Richardson S, Ryan EG. Principles of Experimental Design for Big Data Analysis. Stat Sci 2017; 32:385-404. [PMID: 28883686 PMCID: PMC5584669 DOI: 10.1214/16-sts604] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Big Datasets are endemic, but are often notoriously difficult to analyse because of their size, heterogeneity and quality. The purpose of this paper is to open a discourse on the potential for modern decision theoretic optimal experimental design methods, which by their very nature have traditionally been applied prospectively, to improve the analysis of Big Data through retrospective designed sampling in order to answer particular questions of interest. By appealing to a range of examples, it is suggested that this perspective on Big Data modelling and analysis has the potential for wide generality and advantageous inferential and computational properties. We highlight current hurdles and open research questions surrounding efficient computational optimisation in using retrospective designs, and in part this paper is a call to the optimisation and experimental design communities to work together in the field of Big Data analysis.
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Affiliation(s)
- Christopher C Drovandi
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia, 4000
| | | | - James M McGree
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia, 4000
| | - Kerrie Mengersen
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia, 4000
| | - Sylvia Richardson
- MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, UK, CB2 0SR
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Kang SY, McGree JM, Drovandi CC, Caley MJ, Mengersen KL. Bayesian adaptive design: improving the effectiveness of monitoring of the Great Barrier Reef. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2016; 26:2635-2646. [PMID: 27862584 DOI: 10.1002/eap.1409] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Revised: 06/03/2016] [Accepted: 07/12/2016] [Indexed: 06/06/2023]
Abstract
Monitoring programs are essential for understanding patterns, trends, and threats in ecological and environmental systems. However, such programs are costly in terms of dollars, human resources, and technology, and complex in terms of balancing short- and long-term requirements. In this work, We develop new statistical methods for implementing cost-effective adaptive sampling and monitoring schemes for coral reef that can better utilize existing information and resources, and which can incorporate available prior information. Our research was motivated by developing efficient monitoring practices for Australia's Great Barrier Reef. We develop and implement two types of adaptive sampling schemes, static and sequential, and show that they can be more informative and cost-effective than an existing (nonadaptive) monitoring program. Our methods are developed in a Bayesian framework with a range of utility functions relevant to environmental monitoring. Our results demonstrate the considerable potential for adaptive design to support improved management outcomes in comparison to set-and-forget styles of surveillance monitoring.
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Affiliation(s)
- Su Yun Kang
- Mathematical Sciences School and Institute for Future Environments, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland, 4001, Australia
| | - James M McGree
- Mathematical Sciences School and Institute for Future Environments, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland, 4001, Australia
- ARC Centre of Excellence for Mathematical & Statistical Frontiers, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland, 4001, Australia
| | - Christopher C Drovandi
- Mathematical Sciences School and Institute for Future Environments, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland, 4001, Australia
- ARC Centre of Excellence for Mathematical & Statistical Frontiers, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland, 4001, Australia
| | - M Julian Caley
- ARC Centre of Excellence for Mathematical & Statistical Frontiers, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland, 4001, Australia
- Australian Institute of Marine Science, PMB No.3, Townsville MC, Townsville, Queensland, 4810, Australia
| | - Kerrie L Mengersen
- Mathematical Sciences School and Institute for Future Environments, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland, 4001, Australia
- ARC Centre of Excellence for Mathematical & Statistical Frontiers, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland, 4001, Australia
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Ryan EG, Drovandi CC, McGree JM, Pettitt AN. A Review of Modern Computational Algorithms for Bayesian Optimal Design. Int Stat Rev 2015. [DOI: 10.1111/insr.12107] [Citation(s) in RCA: 105] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Elizabeth G. Ryan
- School of Mathematical Sciences; Queensland University of Technology; Brisbane Australia
- Biostatistics Department, Institute of Psychiatry, Psychology and Neuroscience; King's College London; London UK
| | - Christopher C. Drovandi
- School of Mathematical Sciences; Queensland University of Technology; Brisbane Australia
- ARC Centre of Excellence for Mathematical and Statistical Frontiers; Queensland University of Technology; Brisbane Australia
| | - James M. McGree
- School of Mathematical Sciences; Queensland University of Technology; Brisbane Australia
- ARC Centre of Excellence for Mathematical and Statistical Frontiers; Queensland University of Technology; Brisbane Australia
| | - Anthony N. Pettitt
- School of Mathematical Sciences; Queensland University of Technology; Brisbane Australia
- ARC Centre of Excellence for Mathematical and Statistical Frontiers; Queensland University of Technology; Brisbane Australia
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14
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Abebe HT, Tan FE, Van Breukelen GJ, Berger MP. Bayesian D-optimal designs for the two parameter logistic mixed effects model. Comput Stat Data Anal 2014. [DOI: 10.1016/j.csda.2013.07.040] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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15
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Drovandi CC, McGree, JM, Pettitt AN. A Sequential Monte Carlo Algorithm to Incorporate Model Uncertainty in Bayesian Sequential Design. J Comput Graph Stat 2014. [DOI: 10.1080/10618600.2012.730083] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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16
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Ryan EG, Drovandi CC, Thompson MH, Pettitt AN. Towards Bayesian experimental design for nonlinear models that require a large number of sampling times. Comput Stat Data Anal 2014. [DOI: 10.1016/j.csda.2013.08.017] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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