scholarly journals The Bayesian Superorganism: externalised memories facilitate distributed sampling

2018 ◽  
Author(s):  
Edmund R. Hunt ◽  
Nigel R. Franks ◽  
Roland J. Baddeley
Keyword(s):  
Probability Distributions ◽  
Computational Cost ◽  
Cuticular Hydrocarbon ◽  
Sampling Technique ◽  
Central Place ◽  
Movement Trajectories ◽  
Trail Marking ◽  
Marking Behaviour ◽  
Distributed Sampling ◽  
Collective Information

AbstractA key challenge for any animal (or sampling technique) is to avoid wasting time by searching for resources (information) in places already found to be unprofitable. In biology, this challenge is particularly strong when the organism is a central place forager – returning to a nest between foraging bouts – because it is destined repeatedly to cover much the same ground. This problem will be particularly acute if many individuals forage from the same central place, as in social insects such as the ants. Foraging (sampling) performance may be greatly enhanced by coordinating movement trajectories such that each ant (‘walker’) visits separate parts of the surrounding (unknown) space. We find experimental evidence for an externalised spatial memory in Temnothorax albipennis ants: chemical markers (either pheromones or cues such as cuticular hydrocarbon footprints) that are used by nestmates to mark explored space. We show these markers could be used by the ants to scout the space surrounding their nest more efficiently through indirect coordination. We also develop a simple model of this marking behaviour that can be applied in the context of Markov chain Monte Carlo methods (Baddeley et al. 2019). This substantially enhances the performance of standard methods like the Metropolis–Hastings algorithm in sampling from sparse probability distributions (such as those confronted by the ants) with little additional computational cost. Our Bayesian framework for superorganismal behaviour motivates the evolution of exploratory mechanisms such as trail marking in terms of enhanced collective information processing.

scholarly journals The Bayesian superorganism: externalized memories facilitate distributed sampling

2020 ◽  
Vol 17 (167) ◽  
pp. 20190848
Author(s):  
Edmund R. Hunt ◽  
Nigel R. Franks ◽  
Roland J. Baddeley
Keyword(s):  
Probability Distributions ◽  
Computational Cost ◽  
Cuticular Hydrocarbon ◽  
Sampling Technique ◽  
Central Place ◽  
Movement Trajectories ◽  
Trail Marking ◽  
Marking Behaviour ◽  
Distributed Sampling ◽  
Collective Information

A key challenge for any animal (or sampling technique) is to avoid wasting time by searching for resources (information) in places already found to be unprofitable. In biology, this challenge is particularly strong when the organism is a central place forager—returning to a nest between foraging bouts—because it is destined repeatedly to cover much the same ground. This problem will be particularly acute if many individuals forage from the same central place, as in social insects such as the ants. Foraging (sampling) performance may be greatly enhanced by coordinating movement trajectories such that each ant (walker) visits separate parts of the surrounding (unknown) space. We find experimental evidence for an externalized spatial memory in Temnothorax albipennis ants: chemical markers (either pheromones or cues such as cuticular hydrocarbon footprints) that are used by nest-mates to mark explored space. We show these markers could be used by the ants to scout the space surrounding their nest more efficiently through indirect coordination. We also develop a simple model of this marking behaviour that can be applied in the context of Markov chain Monte Carlo methods (Baddeley et al . 2019 J. R. Soc. Interface 16 , 20190162 ( doi:10.1098/rsif.2019.0162 )). This substantially enhances the performance of standard methods like the Metropolis–Hastings algorithm in sampling from sparse probability distributions (such as those confronted by the ants) with only a little additional computational cost. Our Bayesian framework for superorganismal behaviour motivates the evolution of exploratory mechanisms such as trail marking in terms of enhanced collective information processing.

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.

scholarly journals Remote Sampling with Applications to General Entanglement Simulation

Entropy ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 92
Author(s):  
Gilles Brassard ◽  
Luc Devroye ◽  
Claude Gravel
Keyword(s):  
Initial Phase ◽  
Communication Protocol ◽  
Probability Distributions ◽  
Expected Number ◽  
Trade Off ◽  
Discrete Probability ◽  
Rejection Algorithm ◽  
Distributed Sampling ◽  
Discrete Probability Distributions ◽  
Projective Measurements

We show how to sample exactly discrete probability distributions whose defining parameters are distributed among remote parties. For this purpose, von Neumann’s rejection algorithm is turned into a distributed sampling communication protocol. We study the expected number of bits communicated among the parties and also exhibit a trade-off between the number of rounds of the rejection algorithm and the number of bits transmitted in the initial phase. Finally, we apply remote sampling to the simulation of quantum entanglement in its essentially most general form possible, when an arbitrary finite number m of parties share systems of arbitrary finite dimensions on which they apply arbitrary measurements (not restricted to being projective measurements, but restricted to finitely many possible outcomes). In case the dimension of the systems and the number of possible outcomes per party are bounded by a constant, it suffices to communicate an expected O ( m 2 ) bits in order to simulate exactly the outcomes that these measurements would have produced on those systems.

scholarly journals Communication-Efficient Stochastic Gradient MCMC for Neural Networks

2019 ◽  
Vol 33 ◽  
pp. 4173-4180 ◽  
Author(s):  
Chunyuan Li ◽  
Changyou Chen ◽  
Yunchen Pu ◽  
Ricardo Henao ◽  
Lawrence Carin
Keyword(s):  
Neural Networks ◽  
Probability Distributions ◽  
Computational Cost ◽  
Time Estimation ◽  
Stochastic Gradient ◽  
Communication Overhead ◽  
Test Accuracy ◽  
Training Time ◽  
Learning Probability ◽  
Policy Optimization

Learning probability distributions on the weights of neural networks has recently proven beneficial in many applications. Bayesian methods such as Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) offer an elegant framework to reason about model uncertainty in neural networks. However, these advantages usually come with a high computational cost. We propose accelerating SG-MCMC under the masterworker framework: workers asynchronously and in parallel share responsibility for gradient computations, while the master collects the final samples. To reduce communication overhead, two protocols (downpour and elastic) are developed to allow periodic interaction between the master and workers. We provide a theoretical analysis on the finite-time estimation consistency of posterior expectations, and establish connections to sample thinning. Our experiments on various neural networks demonstrate that the proposed algorithms can greatly reduce training time while achieving comparable (or better) test accuracy/log-likelihood levels, relative to traditional SG-MCMC. When applied to reinforcement learning, it naturally provides exploration for asynchronous policy optimization, with encouraging performance improvement.

Highly Efficient Probabilistic Finite Element Model Updating Using Intelligent Inference With Incomplete Modal Information

2016 ◽  
Vol 138 (5) ◽  
Author(s):  
K. Zhou ◽  
J. Tang
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Model Updating ◽  
Computational Cost ◽  
Sampling Technique ◽  
Element Model ◽  
Measurement Information ◽  
Model Parameters ◽  
Finite Element Model Updating ◽  
Highly Efficient

A highly efficient probabilistic framework of finite element model updating in the presence of measurement noise/uncertainty using intelligent inference is presented. This framework uses incomplete modal measurement information as input and is built upon the Bayesian inference approach. To alleviate the computational cost, Metropolis–Hastings Markov chain Monte Carlo (MH MCMC) is adopted to reduce the size of samples required for repeated finite element modal analyses. Since adopting such a sampling technique in Bayesian model updating usually yields a sparse posterior probability density function (PDF) over the reduced parametric space, Gaussian process (GP) is then incorporated in order to enrich analysis results that can lead to a comprehensive posterior PDF. The PDF obtained with densely distributed data points allows us to find the most optimal model parameters with high fidelity. To facilitate the entire model updating process with automation, the algorithm is implemented under ansys Parametric Design Language (apdl) in ansys environment. The effectiveness of the new framework is demonstrated via systematic case studies.

scholarly journals Updating Short-Term Probabilistic Weather Forecasts of Continuous Variables Using Recent Observations

2011 ◽  
Vol 26 (4) ◽  
pp. 564-571 ◽  
Author(s):  
Thomas N. Nipen ◽  
Greg West ◽  
Roland B. Stull
Keyword(s):  
Recent Observation ◽  
Probability Distributions ◽  
Computational Cost ◽  
Absolute Error ◽  
Transition Function ◽  
Cumulative Distribution ◽  
Continuous Variables ◽  
Probabilistic Forecasts ◽  
Forecasting System ◽  
Highly Correlated

Abstract A statistical postprocessing method for improving probabilistic forecasts of continuous weather variables, given recent observations, is presented. The method updates an existing probabilistic forecast by incorporating observations reported in the intermediary time since model initialization. As such, this method provides updated short-range probabilistic forecasts at an extremely low computational cost. The method models the time sequence of cumulative distribution function (CDF) values corresponding to the observation as a first-order Markov process. Verifying CDF values are highly correlated in time, and their changes in time are modeled probabilistically by a transition function. The effect of the method is that the spread of the probabilistic forecasts for the first few hours after an observation has been made is considerably narrower than the original forecast. The updated probability distributions widen back toward the original forecast for forecast times far in the future as the effect of the recent observation diminishes. The method is tested on probabilistic forecasts produced by an operational ensemble forecasting system. The method improves the ignorance score and the continuous ranked probability score of the probabilistic forecasts significantly for the first few hours after an observation has been made. The mean absolute error of the median of the probability distribution is also shown to be improved.

scholarly journals Constraining subglacial processes from surface velocity observations using surrogate-based Bayesian inference

2021 ◽  
pp. 1-19
Author(s):  
Douglas Brinkerhoff ◽  
Andy Aschwanden ◽  
Mark Fahnestock
Keyword(s):  
Probability Distributions ◽  
Water Pressure ◽  
Model Development ◽  
Computational Cost ◽  
Hydrologic Model ◽  
Surface Velocity ◽  
Small Scale ◽  
Model Parameters ◽  
Stress Regime ◽  
Ice Dynamics

Abstract Basal motion is the primary mechanism for ice flux in Greenland, yet a widely applicable model for predicting it remains elusive. This is due to the difficulty in both observing small-scale bed properties and predicting a time-varying water pressure on which basal motion putatively depends. We take a Bayesian approach to these problems by coupling models of ice dynamics and subglacial hydrology and conditioning on observations of surface velocity in southwestern Greenland to infer the posterior probability distributions for eight spatially and temporally constant parameters governing the behavior of both the sliding law and hydrologic model. Because the model is computationally expensive, characterization of these distributions using classical Markov Chain Monte Carlo sampling is intractable. We skirt this issue by training a neural network as a surrogate that approximates the model at a sliver of the computational cost. We find that surface velocity observations establish strong constraints on model parameters relative to a prior distribution and also elucidate correlations, while the model explains 60% of observed variance. However, we also find that several distinct configurations of the hydrologic system and stress regime are consistent with observations, underscoring the need for continued data collection and model development.

scholarly journals A Method for Class-Imbalance Learning in Android Malware Detection

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3124
Author(s):  
Jun Guan ◽  
Xu Jiang ◽  
Baolei Mao
Keyword(s):  
Machine Learning ◽  
Malware Detection ◽  
Computational Cost ◽  
Class Imbalance ◽  
Sampling Technique ◽  
Minority Class ◽  
Android Malware ◽  
Android Malware Detection ◽  
Imbalance Learning ◽  
Class Imbalance Learning

More and more Android application developers are adopting many different methods against reverse engineering, such as adding a shell, resulting in certain features that cannot be obtained through decompilation, which causes a serious sample imbalance in Android malware detection based on machine learning. Hence, the researchers have focused on how to solve class-imbalance to improve the performance of Android malware detection. However, the disadvantages of the existing class-imbalance learning are mainly the loss of valuable samples and the computational cost. In this paper, we propose a method of Class-Imbalance Learning (CIL), which first selects representative features, uses the clustering K-Means algorithm and under-sampling to retain the important samples of the majority class while reducing the number of samples of the majority class. After that, we use the Synthetic Minority Over-Sampling Technique (SMOTE) algorithm to generate minority class samples for data balance, and finally use the Random Forest (RF) algorithm to build a malware detection model. The result of experiments indicates that CIL effectively improves the performance of Android malware detection based on machine learning, especially for class imbalance. Compared with existing class-imbalance learning methods, CIL is also effective for the Machine Learning Repository from the University of California, Irvine (UCI) and has better performance in some data sets.

scholarly journals Rare Event Sampling Improves Mercury Instability Statistics

2021 ◽  
Vol 923 (2) ◽  
pp. 236
Author(s):  
Dorian S. Abbot ◽  
Robert J. Webber ◽  
Sam Hadden ◽  
Darryl Seligman ◽  
Jonathan Weare
Keyword(s):  
Numerical Simulation ◽  
Computational Cost ◽  
Rare Event ◽  
Sampling Technique ◽  
Direct Numerical Simulations ◽  
Diffusion Monte Carlo ◽  
Probability Estimates ◽  
Planetary Dynamics ◽  
The Future ◽  
Chaotic Nature

Abstract Due to the chaotic nature of planetary dynamics, there is a non-zero probability that Mercury’s orbit will become unstable in the future. Previous efforts have estimated the probability of this happening between 3 and 5 billion years in the future using a large number of direct numerical simulations with an N-body code, but were not able to obtain accurate estimates before 3 billion years in the future because Mercury instability events are too rare. In this paper we use a new rare-event sampling technique, Quantile Diffusion Monte Carlo (QDMC), to estimate that the probability of a Mercury instability event in the next 2 billion years is approximately 10−4 in the REBOUND N-body code. We show that QDMC provides unbiased probability estimates at a computational cost of up to 100 times less than direct numerical simulation. QDMC is easy to implement and could be applied to many problems in planetary dynamics in which it is necessary to estimate the probability of a rare event.

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