Automatic Variational Inference in Stan

Variational inference is a scalable technique for approximate Bayesian inference. Deriving variational inference algorithms requires tedious model-specific calculations; this makes it difficult to automate. We propose an automatic variational inference algorithm, automatic differentiation variational inference (ADVI). The user only provides a Bayesian model and a dataset; nothing else. We make no conjugacy assumptions and support a broad class of models. The algorithm automatically determines an appropriate variational family and optimizes the variational objective. We implement ADVI in Stan (code available now), a probabilistic programming framework. We compare ADVI to MCMC sampling across hierarchical generalized linear models, nonconjugate matrix factorization, and a mixture model. We train the mixture model on a quarter million images. With ADVI we can use variational inference on any model we write in Stan

Automatic Differentiation Variational Inference

Probabilistic modeling is iterative. A scientist posits a simple model, fits it to her data, refines it according to her analysis, and repeats. However, fitting complex models to large data is a bottleneck in this process. Deriving algorithms for new models can be both mathematically and computationally challenging, which makes it difficult to efficiently cycle through the steps. To this end, we develop automatic differentiation variational inference (ADVI). Using our method, the scientist only provides a probabilistic model and a dataset, nothing else. ADVI automatically derives an efficient variational inference algorithm, freeing the scientist to refine and explore many models. ADVI supports a broad class of models-no conjugacy assumptions are required. We study ADVI across ten different models and apply it to a dataset with millions of observations. ADVI is integrated into Stan, a probabilistic programming system; it is available for immediate use

Global atmospheric circulation statistics: Four year averages

Four year averages of the monthly mean global structure of the general circulation of the atmosphere are presented in the form of latitude-altitude, time-altitude, and time-latitude cross sections. The numerical values are given in tables. Basic parameters utilized include daily global maps of temperature and geopotential height for 18 pressure levels between 1000 and 0.4 mb for the period December 1, 1978 through November 30, 1982 supplied by NOAA/NMC. Geopotential heights and geostrophic winds are constructed using hydrostatic and geostrophic formulae. Meridional and vertical velocities are calculated using thermodynamic and continuity equations. Fields presented in this report are zonally averaged temperature, zonal, meridional, and vertical winds, and amplitude of the planetary waves in geopotential height with zonal wave numbers 1-3. The northward fluxes of sensible heat and eastward momentum by the standing and transient eddies along with their wavenumber decomposition and Eliassen-Palm flux propagation vectors and divergences by the standing and transient eddies along with their wavenumber decomposition are also given. Large interhemispheric differences and year-to-year variations are found to originate in the changes in the planetary wave activity

Novel spectral kurtosis technology for adaptive vibration condition monitoring of multi-stage gearboxes

In this paper, the novel wavelet spectral kurtosis (WSK) technique is applied for the early diagnosis of gear tooth faults. Two variants of the wavelet spectral kurtosis technique, called variable resolution WSK and constant resolution WSK, are considered for the diagnosis of pitting gear faults. The gear residual signal, obtained by filtering the gear mesh frequencies, is used as the input to the SK algorithm. The advantages of using the wavelet-based SK techniques when compared to classical Fourier transform (FT)-based SK is confirmed by estimating the toothwise Fisher's criterion of diagnostic features. The final diagnosis decision is made by a three-stage decision-making technique based on the weighted majority rule. The probability of the correct diagnosis is estimated for each SK technique for comparison. An experimental study is presented in detail to test the performance of the wavelet spectral kurtosis techniques and the decision-making technique

Incremental Material Flow Analysis with Bayesian Inference

Material Flow Analysis (MFA) is widely used to study the life-cycles of materials from production, through use, to reuse, recycling or disposal, in order to identify environmental impacts and opportunities to address them. However, development of this type of analysis is often constrained by limited data, which may be uncertain, contradictory, missing or over-aggregated. This article proposes a Bayesian approach, in which uncertain knowledge about material flows is described by probability distributions. If little data is initially available, the model predictions will be rather vague. As new data is acquired, it is systematically incorporated to reduce the level of uncertainty. After reviewing previous approaches to uncertainty in MFA, the Bayesian approach is introduced, and a general recipe for its application to Material Flow Analysis is developed. This is applied to map global production of steel, using Markov Chain Monte Carlo simulations. As well as aiding the analyst, who can get started in the face of incomplete data, this incremental approach to MFA also supports efforts to improve communication of results by transparently accounting for uncertainty throughout.ngineering and Physical Sciences Research Council. Grant Numbers: EP/K039326/1, EP/N02351x/

Gyrations: The Missing Link Between Classical Mechanics with its Underlying Euclidean Geometry and Relativistic Mechanics with its Underlying Hyperbolic Geometry

Being neither commutative nor associative, Einstein velocity addition of relativistically admissible velocities gives rise to gyrations. Gyrations, in turn, measure the extent to which Einstein addition deviates from commutativity and from associativity. Gyrations are geometric automorphisms abstracted from the relativistic mechanical effect known as Thomas precession

THERMAL RADIATION FROM MAGNETIZED NEUTRON STARS: A look at the Surface of a Neutron Star.

Surface thermal emission has been detected by ROSAT from four nearby young neutron stars. Assuming black body emission, the significant pulsations of the observed light curves can be interpreted as due to large surface temperature differences produced by the effect of the crustal magnetic field on the flow of heat from the hot interior toward the cooler surface. However, the energy dependence of the modulation observed in Geminga is incompatible with blackbody emission: this effect will give us a strong constraint on models of the neutron star surface.Comment: 10 pages. tar-compressed and uuencoded postcript file. talk given at the `Jubilee Gamow Seminar', St. Petersburg, Sept. 1994

Triple correlation for detection of damage-related nonlinearities in composite structures

Nonlinear effects in vibration responses are investigated for the undamaged composite plate and the composite plate with a delamination. The analysis is focused on higher harmonic generation in vibration responses for various excitation amplitude levels. This effect is investigated using the triple correlation technique. The dynamics of composite plate was modelled using two-dimensional finite elements and the classical lamination theory. The doubled-node approach was used to model delamination area. Mode shapes and natural frequencies were estimated based on numerical models. Next, the delamination divergence analysis was used to obtain relative displacements for delaminated plies. Experimental modal analysis test was carried out to verify the numerical models. The two strongest vibration modes as well as two vibration modes with the smallest and largest motion level of delaminated plies were selected for nonlinear vibration test. The Fisher criterion was employed to verify the effectiveness and confidence level of the proposed technique. The results show that the method can be used not only to reveal nonlinearities, but also to reliably detect impact damage in composites. These results are confirmed using the statistical analysis

Strongly Coupled Quark Gluon Plasma (SCQGP)

We propose that the reason for the non-ideal behavior seen in lattice simulation of quark gluon plasma (QGP) and relativistic heavy ion collisions (URHICs) experiments is that the QGP near T_c and above is strongly coupled plasma (SCP), i.e., strongly coupled quark gluon plasma (SCQGP). It is remarkable that the widely used equation of state (EoS) of SCP in QED (quantum electrodynamics) very nicely fits lattice results on all QGP systems, with proper modifications to include color degrees of freedom and running coupling constant. Results on pressure in pure gauge, 2-flavors and 3-flavors QGP, are all can be explained by treating QGP as SCQGP as demonstated here.Energy density and speed of sound are also presented for all three systems. We further extend the model to systems with finite quark mass and a reasonably good fit to lattice results are obtained for (2+1)-flavors and 4-flavors QGP. Hence it is the first unified model, namely SCQGP, to explain the non-ideal QGP seen in lattice simulations with just two system dependent parameters.Comment: Revised with corrections and new results, Latex file (11 pages), postscript file of 7 figure