Coherent checking and updating of Bayesian models without specifying the model space: A decision-theoretic semantics for possibility theory

2021 ◽  
Author(s):  
David R. Bickel
Keyword(s):  
Bayesian Models ◽  
Possibility Theory ◽  
Model Space

scholarly journals BUILDING HIGH-PRECISION AMS 14C BAYESIAN MODELS FOR THE FORMATION OF PERI-ABANDONMENT DEPOSITS AT BAKING POT, BELIZE

Radiocarbon ◽  
2021 ◽  
pp. 1-26
Author(s):  
Julie A Hoggarth ◽  
Brendan J Culleton ◽  
Jaime J Awe ◽  
Christophe Helmke ◽  
Sydney Lonaker ◽  
...  
Keyword(s):  
Bayesian Models ◽  
Ninth Century ◽  
Eighth Century ◽  
Classic Maya ◽  
Faunal Remains ◽  
Maya Lowlands ◽  
Human Skeletal Remains ◽  
Late Ninth Century ◽  
Baking Pot ◽  
Group B

ABSTRACT Deposits linked to abandonment have been widely recorded across the Maya lowlands, associated with the final activities occurring in ceremonial areas of Classic Maya centers. Various models have been applied to explain the activities that lie behind the formation of these contexts, including those linked to rapid abandonment (e.g., warfare) and others focused on more protracted events (termination rituals, and/or pilgrimages). Here, we assess Bayesian models for three chronological scenarios of varying tempo to explain the formation of peri-abandonment deposits at Baking Pot, Belize. Using stratigraphic information from these deposits, hieroglyphic dates recovered on artifacts, and direct dates on human skeletal remains and faunal remains from distinct layers in three deposits in Group B at Baking Pot, we identify multiple depositional events that spanned the eighth to ninth centuries AD. These results suggest that the processes associated with the breakdown of institutionalized rulership and its command of labor to construct and maintain ceremonial spaces were protracted at Baking Pot, with evidence for the final depositional activity dated to the mid-to-late ninth century. This interval of deposition was temporally distinct from the earlier deposition(s) in the eighth century. Together, these data offer a detailed view of the end of the Classic period at Baking Pot, in which the ceremonial spaces of the site slowly fell into disuse over a period of more than a century.

scholarly journals Bayesian Models Applied to Cyber Security Anomaly Detection Problems

2021 ◽  
Author(s):  
José A. Perusquía ◽  
Jim E. Griffin ◽  
Cristiano Villa
Keyword(s):  
Anomaly Detection ◽  
Cyber Security ◽  
Bayesian Models

Two-dimensional Bayesian inversion of magnetotelluric data using trans-dimensional Gaussian processes

2021 ◽  
Author(s):  
Daniel Blatter ◽  
Anandaroop Ray ◽  
Kerry Key
Keyword(s):  
Gaussian Process ◽  
Process Model ◽  
Field Data ◽  
Probability Distributions ◽  
Bulk Composition ◽  
Computational Cost ◽  
Model Space ◽  
Bayesian Inversion ◽  
Adaptive Finite Element ◽  
Magnetotelluric Data

Summary Bayesian inversion of electromagnetic data produces crucial uncertainty information on inferred subsurface resistivity. Due to their high computational cost, however, Bayesian inverse methods have largely been restricted to computationally expedient 1D resistivity models. In this study, we successfully demonstrate, for the first time, a fully 2D, trans-dimensional Bayesian inversion of magnetotelluric data. We render this problem tractable from a computational standpoint by using a stochastic interpolation algorithm known as a Gaussian process to achieve a parsimonious parametrization of the model vis-a-vis the dense parameter grids used in numerical forward modeling codes. The Gaussian process links a trans-dimensional, parallel tempered Markov chain Monte Carlo sampler, which explores the parsimonious model space, to MARE2DEM, an adaptive finite element forward solver. MARE2DEM computes the model response using a dense parameter mesh with resistivity assigned via the Gaussian process model. We demonstrate the new trans-dimensional Gaussian process sampler by inverting both synthetic and field magnetotelluric data for 2D models of electrical resistivity, with the field data example converging within 10 days on 148 cores, a non-negligible but tractable computational cost. For a field data inversion, our algorithm achieves a parameter reduction of over 32x compared to the fixed parameter grid used for the MARE2DEM regularized inversion. Resistivity probability distributions computed from the ensemble of models produced by the inversion yield credible intervals and interquartile plots that quantitatively show the non-linear 2D uncertainty in model structure. This uncertainty could then be propagated to other physical properties that impact resistivity including bulk composition, porosity and pore-fluid content.

Assessing the performances of recent global search algorithms using analytic objective functions and seismic optimization problems

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. R767-R781 ◽  
Author(s):  
Mattia Aleardi ◽  
Silvio Pierini ◽  
Angelo Sajeva
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Model Space ◽  
Global Search ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Fireworks Algorithm ◽  
Highly Nonlinear ◽  
Whale Optimization

We have compared the performances of six recently developed global optimization algorithms: imperialist competitive algorithm, firefly algorithm (FA), water cycle algorithm (WCA), whale optimization algorithm (WOA), fireworks algorithm (FWA), and quantum particle swarm optimization (QPSO). These methods have been introduced in the past few years and have found very limited or no applications to geophysical exploration problems thus far. We benchmark the algorithms’ results against the particle swarm optimization (PSO), which is a popular and well-established global search method. In particular, we are interested in assessing the exploration and exploitation capabilities of each method as the dimension of the model space increases. First, we test the different algorithms on two multiminima and two convex analytic objective functions. Then, we compare them using the residual statics corrections and 1D elastic full-waveform inversion, which are highly nonlinear geophysical optimization problems. Our results demonstrate that FA, FWA, and WOA are characterized by optimal exploration capabilities because they outperform the other approaches in the case of optimization problems with multiminima objective functions. Differently, QPSO and PSO have good exploitation capabilities because they easily solve ill-conditioned optimizations characterized by a nearly flat valley in the objective function. QPSO, PSO, and WCA offer a good compromise between exploitation and exploration.

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