1
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Yu W, Bondell HD. Variational Bayes for fast and accurate empirical likelihood inference. J Am Stat Assoc 2023. [DOI: 10.1080/01621459.2023.2169701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Weichang Yu
- School of Mathematics and Statistics, The University of Melbourne
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2
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Moon C, Bedoui A. Bayesian elastic net based on empirical likelihood. J STAT COMPUT SIM 2022. [DOI: 10.1080/00949655.2022.2148254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Chul Moon
- Department of Statistical Science, Southern Methodist University, Dallas, TX, USA
| | - Adel Bedoui
- Department of Statistics, University of Georgia, Athens, GA, USA
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3
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Frazier DT, Nott DJ, Drovandi C, Kohn R. Bayesian inference using synthetic likelihood: asymptotics and adjustments. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2022.2086132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- David T. Frazier
- Department of Econometrics and Business Statistics, Monash University, Clayton VIC 3800, Australia
- Australian Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS)
| | - David J. Nott
- Department of Statistics and Applied Probability, National University of Singapore, Singapore 117546
- Operations Research and Analytics Cluster, National University of Singapore, Singapore 119077
| | - Christopher Drovandi
- School of Mathematical Sciences, Queensland University of Technology, Brisbane 4000 Australia
- Australian Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS)
| | - Robert Kohn
- Australian School of Business, School of Economics, University of New South Wales, Sydney NSW 2052, Australia
- Australian Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS)
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4
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Jahan F, Kennedy DW, Duncan EW, Mengersen KL. Evaluation of spatial Bayesian Empirical Likelihood models in analysis of small area data. PLoS One 2022; 17:e0268130. [PMID: 35622835 PMCID: PMC9140259 DOI: 10.1371/journal.pone.0268130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 04/24/2022] [Indexed: 12/01/2022] Open
Abstract
Bayesian empirical likelihood (BEL) models are becoming increasingly popular as an attractive alternative to fully parametric models. However, they have only recently been applied to spatial data analysis for small area estimation. This study considers the development of spatial BEL models using two popular conditional autoregressive (CAR) priors, namely BYM and Leroux priors. The performance of the proposed models is compared with their parametric counterparts and with existing spatial BEL models using independent Gaussian priors and generalised Moran basis priors. The models are applied to two benchmark spatial datasets, simulation study and COVID-19 data. The results indicate promising opportunities for these models to capture new insights into spatial data. Specifically, the spatial BEL models outperform the parametric spatial models when the underlying distributional assumptions of data appear to be violated.
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Affiliation(s)
- Farzana Jahan
- School of Mathematical Sciences, ARC Centre of Excellence in Mathematical and Statistical Frontiers (ACEMS), QUT Centre for Data Science, Faculty of Science, Queensland University of Technology, Brisbane, Queensland, Australia
- * E-mail:
| | - Daniel W. Kennedy
- School of Mathematical Sciences, ARC Centre of Excellence in Mathematical and Statistical Frontiers (ACEMS), QUT Centre for Data Science, Faculty of Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Earl W. Duncan
- School of Mathematical Sciences, ARC Centre of Excellence in Mathematical and Statistical Frontiers (ACEMS), QUT Centre for Data Science, Faculty of Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Kerrie L. Mengersen
- School of Mathematical Sciences, ARC Centre of Excellence in Mathematical and Statistical Frontiers (ACEMS), QUT Centre for Data Science, Faculty of Science, Queensland University of Technology, Brisbane, Queensland, Australia
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5
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Approximate Bayesian computation using asymptotically normal point estimates. Comput Stat 2022. [DOI: 10.1007/s00180-022-01226-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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6
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Honda T. Optogenetic and thermogenetic manipulation of defined neural circuits and behaviors in Drosophila. Learn Mem 2022; 29:100-109. [PMID: 35332066 PMCID: PMC8973390 DOI: 10.1101/lm.053556.121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 03/06/2022] [Indexed: 11/25/2022]
Abstract
Neural network dynamics underlying flexible animal behaviors remain elusive. The fruit fly Drosophila melanogaster is considered an excellent model in behavioral neuroscience because of its simple neuroanatomical architecture and the availability of various genetic methods. Moreover, Drosophila larvae's transparent body allows investigators to use optical methods on freely moving animals, broadening research directions. Activating or inhibiting well-defined events in excitable cells with a fine temporal resolution using optogenetics and thermogenetics led to the association of functions of defined neural populations with specific behavioral outputs such as the induction of associative memory. Furthermore, combining optogenetics and thermogenetics with state-of-the-art approaches, including connectome mapping and machine learning-based behavioral quantification, might provide a complete view of the experience- and time-dependent variations of behavioral responses. These methodologies allow further understanding of the functional connections between neural circuits and behaviors such as chemosensory, motivational, courtship, and feeding behaviors and sleep, learning, and memory.
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Affiliation(s)
- Takato Honda
- Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
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7
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Liu B, Zhao H, Wang C. Bayesian empirical likelihood of linear regression model with current status data. COMMUN STAT-THEOR M 2022. [DOI: 10.1080/03610926.2022.2044491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Binxia Liu
- College of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, P.R. China
| | - Hui Zhao
- College of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, P.R. China
| | - Chunjie Wang
- College of Mathematics and Statistics, Changchun University of Technology, Changchun, P.R. China
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8
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Abstract
Are olfactory receptor neurons (ORNs) arranged in a functionally meaningful manner to facilitate information processing? Here, we address this long-standing question by uncovering a valence map in the olfactory periphery of Drosophila. Within sensory hairs, we find that neighboring ORNs antagonistically regulate behaviors: stereotypically compartmentalized large- and small-spike ORNs, recognized by their characteristic spike amplitudes, either promote or inhibit the same type of behavior, respectively. Systematic optogenetic and thermogenetic assays—covering the majority of antennal sensilla—highlight a valence-opponent organization. Critically, odor-mixture behavioral experiments show that lateral inhibition between antagonistic ORNs mediates robust behavioral decisions in response to countervailing cues. Computational modeling predicts that the robustness of behavioral output depends on odor mixture ratios. A hallmark of complex sensory systems is the organization of neurons into functionally meaningful maps, which allow for comparison and contrast of parallel inputs via lateral inhibition. However, it is unclear whether such a map exists in olfaction. Here, we address this question by determining the organizing principle underlying the stereotyped pairing of olfactory receptor neurons (ORNs) in Drosophila sensory hairs, wherein compartmentalized neurons inhibit each other via ephaptic coupling. Systematic behavioral assays reveal that most paired ORNs antagonistically regulate the same type of behavior. Such valence opponency is relevant in critical behavioral contexts including place preference, egg laying, and courtship. Odor-mixture experiments show that ephaptic inhibition provides a peripheral means for evaluating and shaping countervailing cues relayed to higher brain centers. Furthermore, computational modeling suggests that this organization likely contributes to processing ratio information in odor mixtures. This olfactory valence map may have evolved to swiftly process ethologically meaningful odor blends without involving costly synaptic computation.
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10
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Bi J, Shen W, Zhu W. Random Forest Adjustment for Approximate Bayesian Computation. J Comput Graph Stat 2021. [DOI: 10.1080/10618600.2021.1981341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Jiefeng Bi
- Wang Yanan Institute for Studies in Economics (WISE), Xiamen University, Xiamen, China
| | - Weining Shen
- Department of Statistics, University of California, Irvine, CA
| | - Weixuan Zhu
- Wang Yanan Institute for Studies in Economics (WISE), Department of Statistics and Data Science, School of Economics, Xiamen University, Xiamen, China
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11
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Lin R, Chan KG, Shi H. A unified Bayesian framework for exact inference of area under the receiver operating characteristic curve. Stat Methods Med Res 2021; 30:2269-2287. [PMID: 34468238 DOI: 10.1177/09622802211037070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The area under the receiver operating characteristic curve is a widely used measure for evaluating the performance of a diagnostic test. Common approaches for inference on area under the receiver operating characteristic curve are usually based upon approximation. For example, the normal approximation based inference tends to suffer from the problem of low accuracy for small sample size. Frequentist empirical likelihood based approaches for area under the receiver operating characteristic curve estimation may perform better, but are usually conducted through approximation in order to reduce the computational burden, thus the inference is not exact. By contrast, we proposed an exact inferential procedure by adapting the empirical likelihood into a Bayesian framework and draw inference from the posterior samples of the area under the receiver operating characteristic curve obtained via a Gibbs sampler. The full conditional distributions within the Gibbs sampler only involve empirical likelihoods with linear constraints, which greatly simplify the computation. To further enhance the applicability and flexibility of the Bayesian empirical likelihood, we extend our method to the estimation of partial area under the receiver operating characteristic curve, comparison of multiple tests, and the doubly robust estimation of area under the receiver operating characteristic curve in the presence of missing test results. Simulation studies confirm the desirable performance of the proposed methods, and a real application is presented to illustrate its usefulness.
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Affiliation(s)
- Ruitao Lin
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, USA
| | - Kc Gary Chan
- Department of Biostatistics, 7284University of Washington, USA
| | - Haolun Shi
- Department of Statistics and Actuarial Science, Simon Fraser University, Canada
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12
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Dmitrieva T, McCullough K, Ebrahimi N. Improved approximate Bayesian computation methods via empirical likelihood. Comput Stat 2021. [DOI: 10.1007/s00180-020-00985-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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13
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Dalla Valle L, Leisen F, Rossini L, Zhu W. A Pólya–Gamma sampler for a generalized logistic regression. J STAT COMPUT SIM 2021. [DOI: 10.1080/00949655.2021.1910947] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Luciana Dalla Valle
- School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth, UK
| | - Fabrizio Leisen
- School of Mathematical Sciences, University of Nottingham, Nottingham, UK
| | - Luca Rossini
- Department of Economics, Management and Quantitative Methods, University of Milan, Milan, Italy
- School of Mathematical Sciences, Queen Mary University of London, London, UK
| | - Weixuan Zhu
- Wang Yanan Institute for Studies in Economics (WISE), Department of Statistics and Data Science, School of Economics, Xiamen University, Xiamen, People's Republic of China
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14
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15
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16
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Dalla Valle L, Leisen F, Rossini L, Zhu W. Bayesian analysis of immigration in Europe with generalized logistic regression. J Appl Stat 2019; 47:424-438. [PMID: 35706962 DOI: 10.1080/02664763.2019.1642310] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
The number of immigrants moving to and settling in Europe has increased over the past decade, making migration one of the most topical and pressing issues in European politics. It is without a doubt that immigration has multiple impacts, in terms of economy, society and culture, on the European Union. It is fundamental to policy-makers to correctly evaluate people's attitudes towards immigration when designing integration policies. Of critical interest is to properly discriminate between subjects who are favourable towards immigration from those who are against it. Public opinions on migration are typically coded as binary responses in surveys. However, traditional methods, such as the standard logistic regression, may suffer from computational issues and are often not able to accurately model survey information. In this paper we propose an efficient Bayesian approach for modelling binary response data based on the generalized logistic regression. We show how the proposed approach provides an increased flexibility compared to traditional methods, due to its ability to capture heavy and light tails. The power of our methodology is tested through simulation studies and is illustrated using European Social Survey data on immigration collected in different European countries in 2016-2017.
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Affiliation(s)
- Luciana Dalla Valle
- School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth, UK
| | - Fabrizio Leisen
- School of Mathematics, Statistics and Actuarial Sciences, University of Kent, Canterbury, UK
| | - Luca Rossini
- Department of Econometrics and Operations Research, VU University Amsterdam, Amsterdam, Netherlands
| | - Weixuan Zhu
- Wang Yanan Institute for Studies in Economics (WISE), Department of Statistics, School of Economics, Xiamen University, Xiamen, People's Republic of China
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17
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Bernton E, Jacob PE, Gerber M, Robert CP. Approximate Bayesian computation with the Wasserstein distance. J R Stat Soc Series B Stat Methodol 2019. [DOI: 10.1111/rssb.12312] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
| | | | | | - Christian P. Robert
- Ceremade, Université Paris‐Dauphine, Université de Recherche Paris Sciences et Lettres France
- University of Warwick Coventry UK
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18
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Bornn L, Shephard N, Solgi R. Moment conditions and Bayesian non-parametrics. J R Stat Soc Series B Stat Methodol 2018. [DOI: 10.1111/rssb.12294] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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19
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Karabatsos G, Leisen F. An approximate likelihood perspective on ABC methods. STATISTICS SURVEYS 2018. [DOI: 10.1214/18-ss120] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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20
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Portilla Yela J, Tovar Cuevas JR. Estimating the Gumbel-Barnett copula parameter of dependence. REVISTA COLOMBIANA DE ESTADÍSTICA 2018. [DOI: 10.15446/rce.v41n1.64900] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
In this paper, we developed an empirical evaluation of four estimation procedures for the dependence parameter of the Gumbel-Barnett copula obtained from a Gumbel type I distribution. We used the maximum likelihood, moments and Bayesian methods and studied the performance of the estimates, assuming three dependence levels and 20 different sample sizes. For each method and scenario, a simulation study was conducted with 1000 runs and the quality of the estimator was evaluated using four different criteria. A Bayesian estimator assuming a Beta(a,b) as prior distribution, showed the best performance regardless the sample size and the dependence structure.
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21
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Meyer X, Chopard B, Salamin N. Accelerating Bayesian inference for evolutionary biology models. Bioinformatics 2017; 33:669-676. [PMID: 28025203 PMCID: PMC5408833 DOI: 10.1093/bioinformatics/btw712] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2016] [Accepted: 11/11/2016] [Indexed: 12/02/2022] Open
Abstract
Motivation Bayesian inference is widely used nowadays and relies largely on Markov chain Monte Carlo (MCMC) methods. Evolutionary biology has greatly benefited from the developments of MCMC methods, but the design of more complex and realistic models and the ever growing availability of novel data is pushing the limits of the current use of these methods. Results We present a parallel Metropolis-Hastings (M-H) framework built with a novel combination of enhancements aimed towards parameter-rich and complex models. We show on a parameter-rich macroevolutionary model increases of the sampling speed up to 35 times with 32 processors when compared to a sequential M-H process. More importantly, our framework achieves up to a twentyfold faster convergence to estimate the posterior probability of phylogenetic trees using 32 processors when compared to the well-known software MrBayes for Bayesian inference of phylogenetic trees. Availability and Implementation https://bitbucket.org/XavMeyer/hogan Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xavier Meyer
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland.,Department of Computer Science, University of Geneva, 1211 Geneva, Switzerland
| | - Bastien Chopard
- Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland.,Department of Computer Science, University of Geneva, 1211 Geneva, Switzerland
| | - Nicolas Salamin
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland
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22
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Affiliation(s)
- Victor De Oliveira
- Department of Management Science and Statistics; The University of Texas at San Antonio; San Antonio, TX 78249 U.S.A
| | - Benjamin Kedem
- Department of Mathematics; University of Maryland; College Park, MD 20742 U.S.A
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23
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Zhu W, Marin JM, Leisen F. A Bootstrap Likelihood Approach to Bayesian Computation. AUST NZ J STAT 2016. [DOI: 10.1111/anzs.12156] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Weixuan Zhu
- Departamento de Estadistica; Universidad Carlos III de Madrid; Calle Madrid 126 28903 Getafe Madrid Spain
| | - J. Miguel Marin
- Departamento de Estadistica; Universidad Carlos III de Madrid; Calle Madrid 126 28903 Getafe Madrid Spain
| | - Fabrizio Leisen
- School of Mathematics, Statistics and Actuarial Science; University of Kent; Canterbury CT2 7NF UK
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24
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Dayaratna KD, Kedem B. Bayesian semiparametric density ratio modelling with applications to medical malpractice reform. STAT MODEL 2016. [DOI: 10.1177/1471082x16641793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
This study examines the efficacy of tort reforms instituted throughout the country during the last decade, improving upon existing semiparametric density ratio estimation (DRE) methodologies in the process. DRE is a well-known semiparametric modelling technique that has been used for well over two decades. Although the approach has been demonstrated to be extremely useful in statistical modelling, it has suffered from one main limitation—the methodology has thus far not been capable of modelling individual-level heterogeneity. We address this issue by presenting a novel adaptation of DRE to model individual level heterogeneity. We do so by marginalizing the associated empirical likelihood function involving density ratios to provide an overall distribution of the entire population despite having extremely limited initial information about each individual in the dataset. We apply this approach to medical malpractice loss data from the previous decade to quantify the probability of changes in tort losses. Our results demonstrate the success of a number of recently implemented malpractice reforms. Comparisons to existing DRE methods, as well as standard regression methods, illustrate the efficacy of our approach.
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Affiliation(s)
- Kevin D Dayaratna
- The Heritage Foundation, Center for Data Analysis, Washington, DC, United States
| | - Benjamin Kedem
- Department of Mathematics, University of Maryland, College Park, MD, USA
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25
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Chaudhuri S, Mondal D, Yin T. Hamiltonian Monte Carlo sampling in Bayesian empirical likelihood computation. J R Stat Soc Series B Stat Methodol 2016. [DOI: 10.1111/rssb.12164] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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26
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Soubeyrand S, Haon-Lasportes E. Weak convergence of posteriors conditional on maximum pseudo-likelihood estimates and implications in ABC. Stat Probab Lett 2015. [DOI: 10.1016/j.spl.2015.08.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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27
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Cheng L, Gilleland E, Heaton MJ, AghaKouchak A. Empirical Bayes estimation for the conditional extreme value model. Stat (Int Stat Inst) 2014. [DOI: 10.1002/sta4.71] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Linyin Cheng
- Center for Hydrometeorology and Remote Sensing; University of California Irvine; Irvine CA 92697 USA
| | - Eric Gilleland
- Research Applications Laboratory; National Center for Atmospheric Research; PO Box 3000 Boulder CO 80307 USA
| | - Matthew J. Heaton
- Department of Statistics; Brigham Young University; Provo UT 84602 USA
| | - Amir AghaKouchak
- Center for Hydrometeorology and Remote Sensing; University of California Irvine; Irvine CA 92697 USA
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Abstract
Mathematical models have been central to ecology for nearly a century. Simple models
of population dynamics have allowed us to understand fundamental aspects underlying
the dynamics and stability of ecological systems. What has remained a challenge,
however, is to meaningfully interpret experimental or observational data in light of
mathematical models. Here, we review recent developments, notably in the growing
field of approximate Bayesian computation (ABC), that allow us to calibrate
mathematical models against available data. Estimating the population demographic
parameters from data remains a formidable statistical challenge. Here, we attempt to
give a flavor and overview of ABC and its applications in population biology and
ecology and eschew a detailed technical discussion in favor of a general discussion
of the advantages and potential pitfalls this framework offers to population
biologists.
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29
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Pham KC, Nott DJ, Chaudhuri S. A note on approximating ABC-MCMC using flexible classifiers. Stat (Int Stat Inst) 2014. [DOI: 10.1002/sta4.56] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Kim Cuc Pham
- Department of Statistics and Applied Probability; National University of Singapore; Singapore 117546 Singapore
| | - David J. Nott
- Department of Statistics and Applied Probability; National University of Singapore; Singapore 117546 Singapore
| | - Sanjay Chaudhuri
- Department of Statistics and Applied Probability; National University of Singapore; Singapore 117546 Singapore
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30
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31
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Rubio FJ, Johansen AM. A simple approach to maximum intractable likelihood estimation. Electron J Stat 2013. [DOI: 10.1214/13-ejs819] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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