@article{cross_2023,
	title = {Credible Mode Determination in Multimodal Economic and Financial Data Distributions},
	journal = {Tinbergen Institute Discussion Paper TI 2023-038/III},
	author = {Cross, Jamie L. and Hoogerheide, Lennart and Labonne, Paul and van Dijk, Herman K.},
	year = {2023},
}

@article{basturk_2023,
	title = {BayesMultiMode: Bayesian Mode Inference in R},
	journal = {Tinbergen Institute Discussion Paper TI 2023-041/III},
	author = {Basturk, Nalan and Cross, Jamie L. and de Knijff, Peter and Hoogerheide, Lennart and Labonne, Paul and van Dijk, Herman K.},
	year = {2023},
}

@article{malsiner-walli_model-based_2016,
	title = {Model-based clustering based on sparse finite {Gaussian} mixtures},
	volume = {26},
	issn = {1573-1375},
	url = {https://doi.org/10.1007/s11222-014-9500-2},
	doi = {10.1007/s11222-014-9500-2},
	language = {en},
	number = {1},
	journal = {Statistics and Computing},
	author = {Malsiner-Walli, Gertraud and Fruhwirth-Schnatter, Sylvia and Grun, Bettina},
	month = jan,
	year = {2016},
	keywords = {Bayesian mixture model, Cluster analysis, Dirichlet prior, Multivariate Gaussian distribution, Normal gamma prior, Sparse modeling},
	pages = {303--324},
}

@article{schaap_genome-wide_2013,
	title = {Genome-wide analysis of macrosatellite repeat copy number variation in worldwide populations: evidence for differences and commonalities in size distributions and size restrictions},
	volume = {14},
	issn = {1471-2164},
	shorttitle = {Genome-wide analysis of macrosatellite repeat copy number variation in worldwide populations},
	url = {https://doi.org/10.1186/1471-2164-14-143},
	doi = {10.1186/1471-2164-14-143},
	number = {1},
	journal = {BMC Genomics},
	author = {Schaap, Mireille and Lemmers, Richard JLF and Maassen, Roel and van der Vliet, Patrick J. and Hoogerheide, Lennart F. and van Dijk, Herman K. and Basturk, Nalan and de Knijff, Peter and van der Maarel, Silvère M.},
	month = mar,
	year = {2013},
	keywords = {Bayes theorem, DNA copy number variations, Population genetics, Tandem repeat sequences},
	pages = {143},
}

@article{Richardson_Green_1997_RJMCMC,
	jstor_articletype = {research-article},
	title = {On {B}ayesian Analysis of Mixtures with an Unknown Number of Components},
	author = {Richardson, Sylvia and Green, Peter J.},
	journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
	jstor_issuetitle = {},
	volume = {59},
	number = {4},
	jstor_formatteddate = {1997},
	pages = {pp. 731--792},
	ISSN = {00359246},
	abstract = {New methodology for fully Bayesian mixture analysis is developed, making use of reversible jump Markov chain Monte Carlo methods that are capable of jumping between the parameter subspaces corresponding to different numbers of components in the mixture. A sample from the full joint distribution of all unknown variables is thereby generated, and this can be used as a basis for a thorough presentation of many aspects of the posterior distribution. The methodology is applied here to the analysis of univariate normal mixtures, using a hierarchical prior model that offers an approach to dealing with weak prior information while avoiding the mathematical pitfalls of using improper priors in the mixture context.},
	language = {English},
	year = {1997},
	publisher = {Wiley for the Royal Statistical Society},
}

@article{SFS:Mal:2019,
	title={From here to infinity: sparse finite versus {D}irichlet process mixtures in model-based clustering},
	author={Fr{\"u}hwirth-Schnatter, Sylvia and {Malsiner-Walli}, Gertraud},
	journal={Advances in Data Analysis and Classification},
	volume = {13},
	pages={33--64},
	year={2019}
}

@article{carreira-perpinan_mode-finding_2000,
	title = {Mode-finding for mixtures of {Gaussian} distributions},
	volume = {22},
	issn = {1939-3539},
	doi = {10.1109/34.888716},
	number = {11},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	author = {Carreira-Perpinan, M.A.},
	month = nov,
	year = {2000},
	note = {Conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence},
	pages = {1318--1323},
}

@article{li_nonparametric_2007,
	title = {A {Nonparametric} {Statistical} {Approach} to {Clustering} via {Mode} {Identification}},
	language = {en},
	author = {Li, Jia and Ray, Surajit and Lindsay, Bruce G.},
	journal={Journal of Machine Learning Research},
	volume={8},
	pages = {1687-1723},
	year={2007}
}

@article{fruhwirth-schnatter_bayesian_2010,
	title = {Bayesian inference for finite mixtures of univariate and multivariate skew-normal and skew-t distributions},
	volume = {11},
	issn = {1465-4644},
	url = {https://doi.org/10.1093/biostatistics/kxp062},
	doi = {10.1093/biostatistics/kxp062},
	number = {2},
	journal = {Biostatistics},
	author = {Frühwirth-Schnatter, Sylvia and Pyne, Saumyadipta},
	month = apr,
	year = {2010},
	pages = {317--336},
}

@article{viallefont2002bayesian,
	title={Bayesian analysis of {Poisson} mixtures},
	author={Viallefont, Val{\'e}rie and Richardson, Sylvia and , Peter J},
	journal={{Journal of Nonparametric Statistics}},
	volume={14},
	number={1-2},
	pages={181--202},
	year={2002},
	publisher={Taylor \& Francis}
}

@article{knapp_international_2018,
	title = {International Best Track Archive for Climate Stewardship (IBTrACS) Project, Version 4},
	doi = {10.1175/2009BAMS2755.1},
	journal = {NOAA National Centers for Environmental Information},
	author = {Knapp, Kenneth R. and Diamond, Howard J. and Kossin J.P. and Kruk, Michael C. and Schreck, C.J.},
	year = {2018},
}

@article{knapp_international_2010,
	title = {The {International} {Best} {Track} {Archive} for {Climate} {Stewardship} ({IBTrACS}): {Unifying} {Tropical} {Cyclone} {Data}},
	volume = {91},
	issn = {0003-0007, 1520-0477},
	shorttitle = {The {International} {Best} {Track} {Archive} for {Climate} {Stewardship} ({IBTrACS})},
	doi = {10.1175/2009BAMS2755.1},
	abstract = {The goal of the International Best Track Archive for Climate Stewardship (IBTrACS) project is to collect the historical tropical cyclone best-track data from all available Regional Specialized Meteorological Centers (RSMCs) and other agencies, combine the disparate datasets into one product, and disseminate in formats used by the tropical cyclone community. Each RSMC forecasts and monitors storms for a specific region and annually archives best-track data, which consist of information on a storm's position, intensity, and other related parameters. IBTrACS is a new dataset based on the best-track data from numerous sources. Moreover, rather than preferentially selecting one track and intensity for each storm, the mean position, the original intensities from the agencies, and summary statistics are provided. This article discusses the dataset construction, explores the tropical cyclone climatology from IBTrACS, and concludes with an analysis of uncertainty in the tropical cyclone intensity record.},
	language = {en},
	number = {3},
	journal = {Bulletin of the American Meteorological Society},
	author = {Knapp, Kenneth R. and Kruk, Michael C. and Levinson, David H. and Diamond, Howard J. and Neumann, Charles J.},
	month = mar,
	year = {2010},
	note = {Publisher: American Meteorological Society
	Section: Bulletin of the American Meteorological Society},
	pages = {363--376},
}

@article{azzalini_1985,
	title = {A {Class} of {Distributions} {Which} {Includes} the {Normal} {Ones}},
	volume = {12},
	issn = {0303-6898},
	abstract = {A new class of density functions depending on a shape parameter λ is introduced, such that λ=0 corresponds to the standard normal density. The properties of this class of density functions are studied.},
	number = {2},
	journal = {Scandinavian Journal of Statistics},
	author = {Azzalini, A.},
	year = {1985},
	note = {Publisher: [Board of the Foundation of the Scandinavian Journal of Statistics, Wiley]},
	pages = {171--178},
}