Deep Gaussian Mixture Models

Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In this work, Deep Gaussian Mixture Models are introduced and discussed. A Deep Gaussian Mixture model (DGMM) is a network of multiple layers of latent variables, where, at each layer, the variables follow a mixture of Gaussian distributions. Thus, the deep mixture model consists of a set of nested mixtures of linear models, which globally provide a nonlinear model able to describe the data in a very flexible way. In order to avoid overparameterized solutions, dimension reduction by factor models can be applied at each layer of the architecture thus resulting in deep mixtures of factor analysers.Comment: 19 pages, 4 figure

Model selection in cosmology

Model selection aims to determine which theoretical models are most plausible given some data, without necessarily considering preferred values of model parameters. A common model selection question is to ask when new data require introduction of an additional parameter, describing a newly discovered physical effect. We review model selection statistics, then focus on the Bayesian evidence, which implements Bayesian analysis at the level of models rather than parameters. We describe our CosmoNest code, the first computationally efficient implementation of Bayesian model selection in a cosmological context. We apply it to recent WMAP satellite data, examining the need for a perturbation spectral index differing from the scaleinvariant (Harrison–Zel'dovich) case

Trans-dimensional inversion of modal dispersion data on the New England Mud Patch

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Bonnel, J., Dosso, S. E., Eleftherakis, D., & Chapman, N. R. Trans-dimensional inversion of modal dispersion data on the New England Mud Patch. IEEE Journal of Oceanic Engineering, 45(1), (2020): 116-130, doi:10.1109/JOE.2019.2896389.This paper presents single receiver geoacoustic inversion of two independent data sets recorded during the 2017 seabed characterization experiment on the New England Mud Patch. In the experimental area, the water depth is around 70 m, and the seabed is characterized by an upper layer of fine grained sediments with clay (i.e., mud). The first data set considered in this paper is a combustive sound source signal, and the second is a chirp emitted by a J15 source. These two data sets provide differing information on the geoacoustic properties of the seabed, as a result of their differing frequency content, and the dispersion properties of the environment. For both data sets, source/receiver range is about 7 km, and modal time-frequency dispersion curves are estimated using warping. Estimated dispersion curves are then used as input data for a Bayesian trans-dimensional inversion algorithm. Subbottom layering and geoacoustic parameters (sound speed and density) are thus inferred from the data. This paper highlights important properties of the mud, consistent with independent in situ measurements. It also demonstrates how information content differs for two data sets collected on reciprocal tracks, but with different acoustic sources and modal content.10.13039/100000006-Office of Naval Research 10.13039/100007297-Office of Naval Research Globa

Investigation of passive atmospheric sounding using millimeter and submillimeter wavelength channels

Activities within the period from January 1, 1992 through June 30, 1992 by Georgia Tech researchers in millimeter and submillimeter wavelength tropospheric remote sensing have been centered around the integration and initial data flights of the MIR on board the NASA ER-2. Georgia Tech contributions during this period include completion of the MIR flight software and implementation of a 'quick-view' graphics program for ground based calibration and analysis of the MIR imagery. In the current configuration, the MIR has channels at 90, 150, 183 +/- 1,3,7, and 220 GHz. Provisions for three additional channels at 325 +/-1,3 and 9 GHZ have been made, and a 325-GHz receiver is currently being built by the ZAX Millimeter Wave Corporation for use in the MIR. The combination of the millimeter wave and submillimeter wave channels aboard a single well-calibrated instrument will provide the necessary aircraft radiometric data for radiative transfer and cloud and water vapor retrieval studies. A paper by the PI discussing the potential benefits of passive millimeter and submillimeter wave observations for cloud, water vapor and precipitation measurements has recently been accepted for publication (Gasiewski, 1992), and is included as Appendix A. The MIR instrument is a joint project between NASA/GSFC and Georgia Tech. Other Georgia Tech contributions to the MIR and its related scientific uses have included basic system design studies, performance analyses, and circuit and radiometric load design