Relevant bibliographies by topics / Gaussian inputs

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Gaussian inputs'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Gaussian inputs"

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Hartman, Eric, and James D. Keeler. "Predicting the Future: Advantages of Semilocal Units." Neural Computation 3, no. 4 (December 1991): 566–78. http://dx.doi.org/10.1162/neco.1991.3.4.566.

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In investigating gaussian radial basis function (RBF) networks for their ability to model nonlinear time series, we have found that while RBF networks are much faster than standard sigmoid unit backpropagation for low-dimensional problems, their advantages diminish in high-dimensional input spaces. This is particularly troublesome if the input space contains irrelevant variables. We suggest that this limitation is due to the localized nature of RBFs. To gain the advantages of the highly nonlocal sigmoids and the speed advantages of RBFs, we propose a particular class of semilocal activation functions that is a natural interpolation between these two families. We present evidence that networks using these gaussian bar units avoid the slow learning problem of sigmoid unit networks, and, very importantly, are more accurate than RBF networks in the presence of irrelevant inputs. On the Mackey-Glass and Coupled Lattice Map problems, the speedup over sigmoid networks is so dramatic that the difference in training time between RBF and gaussian bar networks is minor. Gaussian bar architectures that superpose composed gaussians (gaussians-of-gaussians) to approximate the unknown function have the best performance. We postulate that an interesing behavior displayed by gaussian bar functions under gradient descent dynamics, which we call automatic connection pruning, is an important factor in the success of this representation.
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Srivastava, Ankur, Arun K. Subramaniyan, and Liping Wang. "Analytical global sensitivity analysis with Gaussian processes." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 31, no. 3 (August 2017): 235–50. http://dx.doi.org/10.1017/s0890060417000142.

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AbstractMethods for efficient variance-based global sensitivity analysis of complex high-dimensional problems are presented and compared. Variance decomposition methods rank inputs according to Sobol indices that can be computationally expensive to evaluate. Main and interaction effect Sobol indices can be computed analytically in the Kennedy and O'Hagan framework with Gaussian processes. These methods use the high-dimensional model representation concept for variance decomposition that presents a unique model representation when inputs are uncorrelated. However, when the inputs are correlated, multiple model representations may be possible leading to ambiguous sensitivity ranking with Sobol indices. In this work, we present the effect of input correlation on sensitivity analysis and discuss the methods presented by Li and Rabitz in the context of Kennedy and O'Hagan's framework with Gaussian processes. Results are demonstrated on simulated and real problems for correlated and uncorrelated inputs and demonstrate the utility of variance decomposition methods for sensitivity analysis.
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Koukoulas, P., and N. Kalouptsidis. "Nonlinear system identification using Gaussian inputs." IEEE Transactions on Signal Processing 43, no. 8 (1995): 1831–41. http://dx.doi.org/10.1109/78.403342.

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Schwartz, Odelia, Terrence J. Sejnowski, and Peter Dayan. "Soft Mixer Assignment in a Hierarchical Generative Model of Natural Scene Statistics." Neural Computation 18, no. 11 (November 2006): 2680–718. http://dx.doi.org/10.1162/neco.2006.18.11.2680.

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Gaussian scale mixture models offer a top-down description of signal generation that captures key bottom-up statistical characteristics of filter responses to images. However, the pattern of dependence among the filters for this class of models is prespecified. We propose a novel extension to the gaussian scale mixturemodel that learns the pattern of dependence from observed inputs and thereby induces a hierarchical representation of these inputs. Specifically, we propose that inputs are generated by gaussian variables (modeling local filter structure), multiplied by a mixer variable that is assigned probabilistically to each input from a set of possible mixers. We demonstrate inference of both components of the generative model, for synthesized data and for different classes of natural images, such as a generic ensemble and faces. For natural images, the mixer variable assignments show invariances resembling those of complex cells in visual cortex; the statistics of the gaussian components of the model are in accord with the outputs of divisive normalization models. We also show how our model helps interrelate a wide range of models of image statistics and cortical processing.
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Wong, P. W., and R. M. Gray. "Sigma-delta modulation with i.i.d. Gaussian inputs." IEEE Transactions on Information Theory 36, no. 4 (July 1990): 784–98. http://dx.doi.org/10.1109/18.53738.

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Xu-Friedman, Matthew A., and Wade G. Regehr. "Dynamic-Clamp Analysis of the Effects of Convergence on Spike Timing. II. Few Synaptic Inputs." Journal of Neurophysiology 94, no. 4 (October 2005): 2526–34. http://dx.doi.org/10.1152/jn.01308.2004.

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Sensory pathways in the nervous system possess mechanisms for decreasing spike-timing variability (“jitter”), probably to increase acuity. Most studies of jitter reduction have focused on convergence of many subthreshold inputs. However, many neurons receive only a few active inputs at any given time, and jitter reduction under these conditions is not well understood. We examined this issue using dynamic-clamp recordings in slices from mouse auditory brain stem. Significant jitter reduction was possible with as few as two inputs, provided the inputs had several features. First, jitter reduction was greatest and most reliable for supra-threshold inputs. Second, significant jitter reduction occurred when the distribution of input times had a rapid onset, i.e., for alpha- but not for Gaussian-distributed inputs. Third, jitter reduction was compromised unless late inputs were suppressed by the refractory period of the cell. These results contrast with the finding in the previous paper in which many subthreshold inputs contribute to jitter reduction, whether alpha- or Gaussian-distributed. In addition, convergence of many subthreshold inputs could fail to elicit any postsynaptic response when the input distribution outlasted the refractory period of the cell. These significant differences indicate that each means of reducing jitter has advantages and disadvantages and may be more effective for different neurons depending on the properties of their inputs.
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CARD, HOWARD C. "STOCHASTIC RADIAL BASIS FUNCTIONS." International Journal of Neural Systems 11, no. 02 (April 2001): 203–10. http://dx.doi.org/10.1142/s0129065701000552.

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Stochastic signal processing can implement gaussian activation functions for radial basis function networks, using stochastic counters. The statistics of neural inputs which control the increment and decrement operations of the counter are governed by Bernoulli distributions. The transfer functions relating the input and output pulse probabilities can closely approximate gaussian activation functions which improve with the number of states in the counter. The means and variances of these gaussian approximations can be controlled by varying the output combinational logic function of the binary counter variables.
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Mena-Parra, J., K. Bandura, M. A. Dobbs, J. R. Shaw, and S. Siegel. "Quantization Bias for Digital Correlators." Journal of Astronomical Instrumentation 07, no. 02n03 (September 2018): 1850008. http://dx.doi.org/10.1142/s2251171718500083.

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In radio interferometry, the quantization process introduces a bias in the magnitude and phase of the measured correlations which translates into errors in the measurement of source brightness and position in the sky, affecting both the system calibration and image reconstruction. In this paper, we investigate the biasing effect of quantization in the measured correlation between complex-valued inputs with a circularly symmetric Gaussian probability density function (PDF), which is the typical case for radio astronomy applications. We start by calculating the correlation between the input and quantization error and its effect on the quantized variance, first in the case of a real-valued quantizer with a zero mean Gaussian input and then in the case of a complex-valued quantizer with a circularly symmetric Gaussian input. We demonstrate that this input-error correlation is always negative for a quantizer with an odd number of levels, while for an even number of levels, this correlation is positive in the low signal level regime. In both cases, there is an optimal interval for the input signal level for which this input-error correlation is very weak and the model of additive uncorrelated quantization noise provides a very accurate approximation. We determine the conditions under which the magnitude and phase of the measured correlation have negligible bias with respect to the unquantized values: we demonstrate that the magnitude bias is negligible only if both unquantized inputs are optimally quantized (i.e. when the uncorrelated quantization error model is valid), while the phase bias is negligible when (1) at least one of the inputs is optimally quantized, or when (2) the correlation coefficient between the unquantized inputs is small. Finally, we determine the implications of these results for radio interferometry.
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Xu-Friedman, Matthew A., and Wade G. Regehr. "Dynamic-Clamp Analysis of the Effects of Convergence on Spike Timing. I. Many Synaptic Inputs." Journal of Neurophysiology 94, no. 4 (October 2005): 2512–25. http://dx.doi.org/10.1152/jn.01307.2004.

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Precise action potential timing is crucial in sensory acuity and motor control. Convergence of many synaptic inputs is thought to provide a means of decreasing spike-timing variability (“jitter”), but its effectiveness has never been tested in real neurons. We used the dynamic-clamp technique in mouse auditory brain stem slices to examine how convergence controls spike timing. We tested the roles of several synaptic properties that are influenced by ongoing activity in vivo: the number of active inputs ( N), their total synaptic conductance ( Gtot), and their timing, which can resemble an alpha or a Gaussian distribution. Jitter was reduced most with large N, up to a factor of over 20. Variability in N is likely to occur in vivo, but this added little jitter. Jitter reduction also depended on the timing of inputs: alpha-distributed inputs were more effective than Gaussian-distributed inputs. Furthermore, the two distributions differed in their sensitivity to synaptic conductance: for Gaussian-distributed inputs, jitter was most reduced when Gtot was 2–3 times threshold, whereas alpha-distributed inputs showed continued jitter reduction with higher Gtot. However, very high Gtot caused the postsynaptic cell to fire multiple times, particularly when the input jitter outlasted the cell's refractory period, which interfered with jitter reduction. Gtot also greatly affected the response latency, which could influence downstream computations. Changes in Gtot are likely to arise in vivo through activity-dependent changes in synaptic strength. High rates of postsynaptic activity increased the number of synaptic inputs required to evoke a postsynaptic response. Despite this, jitter was still effectively reduced, particularly in cases when this increased threshold eliminated secondary spikes. Thus these studies provide insight into how specific features of converging inputs control spike timing.
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Bachoc, Francois, Fabrice Gamboa, Jean-Michel Loubes, and Nil Venet. "A Gaussian Process Regression Model for Distribution Inputs." IEEE Transactions on Information Theory 64, no. 10 (October 2018): 6620–37. http://dx.doi.org/10.1109/tit.2017.2762322.

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Dissertations / Theses on the topic "Gaussian inputs"

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Le, Anh Duc. "Fundamental Limits of Communication Channels under Non-Gaussian Interference." University of Akron / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=akron1469011496.

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Cuesta, Ramirez Jhouben Janyk. "Optimization of a computationally expensive simulator with quantitative and qualitative inputs." Thesis, Lyon, 2022. http://www.theses.fr/2022LYSEM010.

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Dans cette thèse, les problèmes mixtes couteux sont abordés par le biais de processus gaussiens où les variables discrètes sont relaxées en variables latentes continues. L'espace continu est plus facilement exploité par les techniques classiques d'optimisation bayésienne que ne le serait un espace mixte. Les variables discrètes sont récupérées soit après l'optimisation continue, soit simultanément avec une contrainte supplémentaire de compatibilité continue-discrète qui est traitée avec des lagrangiens augmentés. Plusieurs implémentations possibles de ces optimiseurs mixtes bayésiens sont comparées. En particulier, la reformulation du problème avec des variables latentes continues est mise en concurrence avec des recherches travaillant directement dans l'espace mixte. Parmi les algorithmes impliquant des variables latentes et un lagrangien augmenté, une attention particulière est consacrée aux multiplicateurs de lagrange pour lesquels des techniques d'estimation locale et globale sont étudiées. Les comparaisons sont basées sur l'optimisation répétée de trois fonctions analytiques et sur une application mécanique concernant la conception d'une poutre. Une étude supplémentaire pour l'application d'une stratégie d'optimisation mixte proposée dans le domaine de l'auto-calibrage mixte est faite. Cette analyse s'inspire d'une application de quantification des radionucléides, qui définit une fonction inverse spécifique nécessitant l'étude de ses multiples propriétés dans le scenario continu. une proposition de différentes stratégies déterministes et bayésiennes a été faite en vue d'une définition complète dans un contexte de variables mixtes
In this thesis, costly mixed problems are approached through gaussian processes where the discrete variables are relaxed into continuous latent variables. the continuous space is more easily harvested by classical bayesian optimization techniques than a mixed space would. discrete variables are recovered either subsequently to the continuous optimization, or simultaneously with an additional continuous-discrete compatibility constraint that is handled with augmented lagrangians. several possible implementations of such bayesian mixed optimizers are compared. in particular, the reformulation of the problem with continuous latent variables is put in competition with searches working directly in the mixed space. among the algorithms involving latent variables and an augmented lagrangian, a particular attention is devoted to the lagrange multipliers for which a local and a global estimation techniques are studied. the comparisons are based on the repeated optimization of three analytical functions and a mechanical application regarding a beam design. an additional study for applying a proposed mixed optimization strategy in the field of mixed self-calibration is made. this analysis was inspired in an application in radionuclide quantification, which defined an specific inverse function that required the study of its multiple properties in the continuous scenario. a proposition of different deterministic and bayesian strategies was made towards a complete definition in a mixed variable setup
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Zhang, Yulei. "Computer Experiments with Both Quantitative and Qualitative Inputs." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1408042133.

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Betancourt, José. "Functional-input metamodeling : an application to coastal flood early warning." Thesis, Toulouse 3, 2020. http://www.theses.fr/2020TOU30097.

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Les inondations en général affectent plus de personnes que tout autre catastrophe. Au cours de la dernière décennie du 20ème siècle, plus de 1.5 milliard de personnes ont été affectées. Afin d'atténuer l'impact de ce type de catastrophe, un effort scientifique significatif a été consacré à la constitution de codes de simulation numériques pour la gestion des risques. Les codes disponibles permettent désormais de modéliser correctement les événements d'inondation côtière à une résolution assez élevée. Malheureusement, leur utilisation est fortement limitée pour l'alerte précoce, avec une simulation de quelques heures de dynamique maritime prenant plusieurs heures à plusieurs jours de temps de calcul. Cette thèse fait partie du projet ANR RISCOPE, qui vise à remédier cette limitation en construisant des métamodèles pour substituer les codes hydrodynamiques coûteux en temps de calcul. En tant qu'exigence particulière de cette application, le métamodèle doit être capable de traiter des entrées fonctionnelles correspondant à des conditions maritimes variant dans le temps. À cette fin, nous nous sommes concentrés sur les métamodèles de processus Gaussiens, développés à l'origine pour des entrées scalaires, mais maintenant disponibles aussi pour des entrées fonctionnelles. La nature des entrées a donné lieu à un certain nombre de questions sur la bonne façon de les représenter dans le métamodèle: (i) quelles entrées fonctionnelles méritent d'être conservées en tant que prédicteurs, (ii) quelle méthode de réduction de dimension (e.g., B-splines, PCA, PLS) est idéale, (iii) quelle est une dimension de projection appropriée, et (iv) quelle est une distance adéquate pour mesurer les similitudes entre les points d'entrée fonctionnels dans la fonction de covariance. Certaines de ces caractéristiques - appelées ici paramètres structurels - du modèle et d'autres telles que la famille de covariance (e.g., Gaussien, Matérn 5/2) sont souvent arbitrairement choisies a priori. Comme nous l'avons montré à travers des expériences, ces décisions peuvent avoir un fort impact sur la capacité de prédiction du métamodèle. Ainsi, sans perdre de vue notre but de contribuer à l'amélioration de l'alerte précoce des inondations côtières, nous avons entrepris la construction d'une méthodologie efficace pour définir les paramètres structurels du modèle. Comme première solution, nous avons proposé une approche d'exploration basée sur la Méthodologie de Surface de Réponse. Elle a été utilisé efficacement pour configurer le métamodèle requis pour une fonction de test analytique, ainsi que pour une version simplifiée du code étudié dans RISCOPE. Bien que relativement simple, la méthodologie proposée a pu trouver des configurations de métamodèles de capacité de prédiction élevée avec des économies allant jusqu'à 76.7% et 38.7% du temps de calcul utilisé par une approche d'exploration exhaustive dans les deux cas étudiés. [...]
Currently, floods in general affect more people than any other hazard. In just the last decade of the 20th century, more than 1.5 billion were affected. In the seek to mitigate the impact of this type of hazard, strong scientific effort has been devoted to the constitution of computer codes that could be used as risk management tools. Available computer models now allow properly modelling coastal flooding events at a fairly high resolution. Unfortunately, their use is strongly prohibitive for early warning, with a simulation of few hours of maritime dynamics taking several hours to days of processing time, even on multi-processor clusters. This thesis is part of the ANR RISCOPE project, which aims at addressing this limitation by means of surrogate modeling of the hydrodynamic computer codes. As a particular requirement of this application, the metamodel should be able to deal with functional inputs corresponding to time varying maritime conditions. To this end, we focused on Gaussian process metamodels, originally developed for scalar inputs, but now available also for functional inputs. The nature of the inputs gave rise to a number of questions about the proper way to represent them in the metamodel: (i) which functional inputs are worth keeping as predictors, (ii) which dimension reduction method (e.g., B-splines, PCA, PLS) is ideal, (iii) which is a suitable projection dimension, and given our choice to work with Gaussian process metamodels, also the question of (iv) which is a convenient distance to measure similarities between functional input points within the kernel function. Some of these characteristics - hereon called structural parameters - of the model and some others such as the family of kernel (e.g., Gaussian, Matérn 5/2) are often arbitrarily chosen a priori. Sometimes, those are selected based on other studies. As one may intuit and has been shown by us through experiments, those decisions could have a strong impact on the prediction capability of the resulting model. Thus, without losing sight of our final goal of contributing to the improvement of coastal flooding early warning, we undertook the construction of an efficient methodology to set up the structural parameters of the model. As a first solution, we proposed an exploration approach based on the Response Surface Methodology. It was effectively used to tune the metamodel for an analytic toy function, as well as for a simplified version of the code studied in RISCOPE. While relatively simple, the proposed methodology was able to find metamodel configurations of high prediction capability with savings of up to 76.7% and 38.7% of the time spent by an exhaustive search approach in the analytic case and coastal flooding case, respectively. The solution found by our methodology was optimal in most cases. We developed later a second prototype based on Ant Colony Optimization (ACO). This new approach is more powerful in terms of solution time and flexibility in the features of the model allowed to be explored.[...]
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Ptáček, Martin. "Spatial Function Estimation with Uncertain Sensor Locations." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-449288.

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Tato práce se zabývá úlohou odhadování prostorové funkce z hlediska regrese pomocí Gaussovských procesů (GPR) za současné nejistoty tréninkových pozic (pozic senzorů). Nejdříve je zde popsána teorie v pozadí GPR metody pracující se známými tréninkovými pozicemi. Tato teorie je poté aplikována při odvození výrazů prediktivní distribuce GPR v testovací pozici při uvážení nejistoty tréninkových pozic. Kvůli absenci analytického řešení těchto výrazů byly výrazy aproximovány pomocí metody Monte Carlo. U odvozené metody bylo demonstrováno zlepšení kvality odhadu prostorové funkce oproti standardnímu použití GPR metody a také oproti zjednodušenému řešení uvedenému v literatuře. Dále se práce zabývá možností použití metody GPR s nejistými tréninkovými pozicemi v~kombinaci s výrazy s dostupným analytickým řešením. Ukazuje se, že k dosažení těchto výrazů je třeba zavést značné předpoklady, což má od počátku za následek nepřesnost prediktivní distribuce. Také se ukazuje, že výsledná metoda používá standardní výrazy GPR v~kombinaci s upravenou kovarianční funkcí. Simulace dokazují, že tato metoda produkuje velmi podobné odhady jako základní GPR metoda uvažující známé tréninkové pozice. Na druhou stranu prediktivní variance (nejistota odhadu) je u této metody zvýšena, což je žádaný efekt uvážení nejistoty tréninkových pozic.
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Sarkar, Avik. "The Capacity Region of the Gaussian Z-Interference Channel with Gaussian Input and Weak Interference." Thesis, North Dakota State University, 2016. https://hdl.handle.net/10365/27981.

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We consider a wireless communication scenario with two transmit-receive pairs where each of the transmitters has a message for its corresponding receiver and only one of the receivers face interference from the undesired transmitter. In our research, we focused on devising optimal ways to manage this undesired interference and characterize the best communication rates for both transmit-receive pairs. Currently, this problem of interference is dealt with by restricting the two communications in di erent frequency or time bands. We explore the possibility of achieving better rates by allowing them to operate in the same band. Such channels were identi ed about 4 decades ago, but the maximum rate of communication when the transmitters have a power constraint is still unknown. In this work, we characterize the best rates for this channel under a reasonable practical constraint of using Gaussian signals at both the transmitters.
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Zhang, Boya. "Computer Experimental Design for Gaussian Process Surrogates." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99886.

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With a rapid development of computing power, computer experiments have gained popularity in various scientific fields, like cosmology, ecology and engineering. However, some computer experiments for complex processes are still computationally demanding. A surrogate model or emulator, is often employed as a fast substitute for the simulator. Meanwhile, a common challenge in computer experiments and related fields is to efficiently explore the input space using a small number of samples, i.e., the experimental design problem. This dissertation focuses on the design problem under Gaussian process surrogates. The first work demonstrates empirically that space-filling designs disappoint when the model hyperparameterization is unknown, and must be estimated from data observed at the chosen design sites. A purely random design is shown to be superior to higher-powered alternatives in many cases. Thereafter, a new family of distance-based designs are proposed and their superior performance is illustrated in both static (one-shot design) and sequential settings. The second contribution is motivated by an agent-based model(ABM) of delta smelt conservation. The ABM is developed to assist in a study of delta smelt life cycles and to understand sensitivities to myriad natural variables and human interventions. However, the input space is high-dimensional, running the simulator is time-consuming, and its outputs change nonlinearly in both mean and variance. A batch sequential design scheme is proposed, generalizing one-at-a-time variance-based active learning, as a means of keeping multi-core cluster nodes fully engaged with expensive runs. The acquisition strategy is carefully engineered to favor selection of replicates which boost statistical and computational efficiencies. Design performance is illustrated on a range of toy examples before embarking on a smelt simulation campaign and downstream high-fidelity input sensitivity analysis.
Doctor of Philosophy
With a rapid development of computing power, computer experiments have gained popularity in various scientific fields, like cosmology, ecology and engineering. However, some computer experiments for complex processes are still computationally demanding. Thus, a statistical model built upon input-output observations, i.e., a so-called surrogate model or emulator, is needed as a fast substitute for the simulator. Design of experiments, i.e., how to select samples from the input space under budget constraints, is also worth studying. This dissertation focuses on the design problem under Gaussian process (GP) surrogates. The first work demonstrates empirically that commonly-used space-filling designs disappoint when the model hyperparameterization is unknown, and must be estimated from data observed at the chosen design sites. Thereafter, a new family of distance-based designs are proposed and their superior performance is illustrated in both static (design points are allocated at one shot) and sequential settings (data are sampled sequentially). The second contribution is motivated by a stochastic computer simulator of delta smelt conservation. This simulator is developed to assist in a study of delta smelt life cycles and to understand sensitivities to myriad natural variables and human interventions. However, the input space is high-dimensional, running the simulator is time-consuming, and its outputs change nonlinearly in both mean and variance. An innovative batch sequential design method is proposed, generalizing one-at-a-time sequential design to one-batch-at-a-time scheme with the goal of parallel computing. The criterion for subsequent data acquisition is carefully engineered to favor selection of replicates which boost statistical and computational efficiencies. The design performance is illustrated on a range of toy examples before embarking on a smelt simulation campaign and downstream input sensitivity analysis.
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Ralston, Jonathon Carey. "Identification of a class of nonlinear systems in the non-Gaussian input case." Thesis, Queensland University of Technology, 1996. https://eprints.qut.edu.au/36000/2/36000_Digitised_Thesis.pdf.

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Kim, Nungsoo. "Extraction of the second-order nonlinear response from model test data in random seas and comparison of the Gaussian and non-Gaussian models." Texas A&M University, 2004. http://hdl.handle.net/1969.1/3183.

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This study presents the results of an extraction of the 2nd-order nonlinear responses from model test data. Emphasis is given on the effects of assumptions made for the Gaussian and non-Gaussian input on the estimation of the 2nd-order response, employing the quadratic Volterra model. The effects of sea severity and data length on the estimation of response are also investigated at the same time. The data sets used in this study are surge forces on a fixed barge, a surge motion of a compliant mini TLP (Tension Leg Platform), and surge forces on a fixed and truncated column. Sea states are used from rough sea (Hs=3m) to high sea (Hs=9m) for a barge case, very rough sea (Hs=3.9m) for a mini TLP, and phenomenal sea (Hs=15m) for a truncated column. After the estimation of the response functions, the outputs are reconstructed and the 2nd order nonlinear responses are extracted with all the QTF distributed in the entire bifrequency domain. The reconstituted time series are compared with the experiment in both the time and frequency domains. For the effects of data length on the estimation of the response functions, 3, 15, and 40- hour data were investigated for a barge, but 3-hour data was used for a mini TLP and a fixed and truncated column due to lack of long data. The effects of sea severity on the estimation of the response functions are found in both methods. The non-Gaussian method for estimation is more affected by data length than the Gaussian method.
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Han, Gang. "Modeling the output from computer experiments having quantitative and qualitative input variables and its applications." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1228326460.

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Books on the topic "Gaussian inputs"

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Petersen, William B. Inpuff 2.0--a multiple source Gaussian puff dispersion algorithm user's guide. Research Triangle Park, NC: U.S. Environmental Protection Agency, Atmospheric Sciences Research Laboratory, 1987.

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Petersen, William B. Inpuff 2.0--a multiple source Gaussian puff dispersion algorithm user's guide. Research Triangle Park, NC: U.S. Environmental Protection Agency, Atmospheric Sciences Research Laboratory, 1987.

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Petersen, William B. Inpuff 2.0--a multiple source Gaussian puff dispersion algorithm user's guide. Research Triangle Park, NC: U.S. Environmental Protection Agency, Atmospheric Sciences Research Laboratory, 1987.

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Petersen, William B. Inpuff 2.0--a multiple source Gaussian puff dispersion algorithm user's guide. Research Triangle Park, NC: U.S. Environmental Protection Agency, Atmospheric Sciences Research Laboratory, 1987.

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Petersen, William B. Inpuff 2.0--a multiple source Gaussian puff dispersion algorithm user's guide. Research Triangle Park, NC: U.S. Environmental Protection Agency, Atmospheric Sciences Research Laboratory, 1987.

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Gary, Rosen I., and Institute for Computer Applications in Science and Engineering., eds. Approximation of discrete-time LQG compensators for distributed systems with boundary input and unbounded measurement. Hampton, Va: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1987.

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Lee, Herbert K. H., Matthew Taddy, Robert Gramacy, and Genetha Gray. Designing and analysing a circuit device experiment using treed Gaussian processes. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.28.

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This article describes a new circuit device, developed in collaboration with scientists at Sandia National Laboratories, based on treed Gaussian processes (TGP). The circuit devices under study are bipolar junction transistors, which are used to amplify electrical current. To aid with the design of the device, a computer model predicts its peak output as a function of the input dosage and a number of design parameters. The methodology also involves a novel sequential design procedure to generate data to fit the emulator. Both physical and computer simulation experiments are performed, and the results show that the TGP model can be useful for spatial data and semiparametric regression in the context of a computer experiment for designing a circuit device, for sequential design of (computer) experiments, sequential robust local optimization, validation, calibration, and sensitivity analysis.
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Book chapters on the topic "Gaussian inputs"

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Mandjes, Michel. "Queueing Networks with Gaussian Inputs." In International Series in Operations Research & Management Science, 531–60. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-6472-4_12.

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Bock, Sebastian, Philipp Schwarz, and Martin G. Weiß. "U-Shape Phenomenon with Gaussian Noise and Clipped Inputs." In Proceedings of Eighth International Congress on Information and Communication Technology, 569–79. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3043-2_45.

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Tomczak, Jakub M. "Gaussian Process Regression with Categorical Inputs for Predicting the Blood Glucose Level." In Advances in Intelligent Systems and Computing, 98–108. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48944-5_10.

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Wyber, R. J. "The Design of Optimal Processors for Arrays with Non-Gaussian Noise Inputs." In Adaptive Methods in Underwater Acoustics, 515–26. Dordrecht: Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-009-5361-1_46.

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Hashemi, Vahid, Jan Křetínský, Stefanie Mohr, and Emmanouil Seferis. "Gaussian-Based Runtime Detection of Out-of-distribution Inputs for Neural Networks." In Runtime Verification, 254–64. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88494-9_14.

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Bijak, Jakub, and Jason Hilton. "Uncertainty Quantification, Model Calibration and Sensitivity." In Towards Bayesian Model-Based Demography, 71–92. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83039-7_5.

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AbstractBetter understanding of the behaviour of agent-based models, aimed at embedding them in the broader, model-based line of scientific enquiry, requires a comprehensive framework for analysing their results. Seeing models as tools for experimenting in silico, this chapter discusses the basic tenets and techniques of uncertainty quantification and experimental design, both of which can help shed light on the workings of complex systems embedded in computational models. In particular, we look at: relationships between model inputs and outputs, various types of experimental design, methods of analysis of simulation results, assessment of model uncertainty and sensitivity, which helps identify the parts of the model that matter in the experiments, as well as statistical tools for calibrating models to the available data. We focus on the role of emulators, or meta-models – high-level statistical models approximating the behaviour of the agent-based models under study – and in particular, on Gaussian processes (GPs). The theoretical discussion is illustrated by applications to the Routes and Rumours model of migrant route formation introduced before.
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Cook, A., O. Rondon, J. Graindorge, and G. Booth. "Iterative Gaussianisation for Multivariate Transformation." In Springer Proceedings in Earth and Environmental Sciences, 21–35. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19845-8_2.

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AbstractMultivariate conditional simulations can be reduced to a set of independent univariate simulations through multivariate Gaussian transformation of the drill hole data to independent Gaussian factors. These simulations are then back transformed to obtain simulated results that exhibit the multivariate relationships observed in the input drill hole data. Several transformation techniques are cited in geostatistical literature for multivariate transformation. However, only two can effectively simulate high dimensional drill hole data with complex non-linear features: Flow Anamorphosis (FA) and Projection Pursuit Multivariate Transformation (PPMT). This paper presents an alternative iterative multivariate Gaussian transformation (IG) along with a multivariate simulation case study of a large Nickel deposit. Our findings show that IG is computationally faster than FA and PPMT which makes the technique more appealing for most practical and time-sensitive applications.
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Vinokur, Igor, and David Tolpin. "Warped Input Gaussian Processes for Time Series Forecasting." In Lecture Notes in Computer Science, 205–20. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78086-9_16.

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Muscolino, G. "Response of Linear and Non-Linear Structural Systems under Gaussian or Non-Gaussian Filtered Input." In Dynamic Motion: Chaotic and Stochastic Behaviour, 203–99. Vienna: Springer Vienna, 1993. http://dx.doi.org/10.1007/978-3-7091-2682-0_6.

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Sandberg, Irwin W. "Approximation of Input-Output Maps using Gaussian Radial Basis Functions." In Stability and Control of Dynamical Systems with Applications, 155–66. Boston, MA: Birkhäuser Boston, 2003. http://dx.doi.org/10.1007/978-1-4612-0037-6_8.

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Conference papers on the topic "Gaussian inputs"

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Rodrigues, Miguel R. D., Fernando Perez-Cruz, and Sergio Verduy. "Multiple-input multiple-output Gaussian channels: Optimal covariance for non-Gaussian inputs." In 2008 IEEE Information Theory Workshop (ITW). IEEE, 2008. http://dx.doi.org/10.1109/itw.2008.4578704.

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Cao, Wei, Alex Dytso, Michael Fauss, Gang Feng, and H. Vincent Poor. "Robust Waterfilling for Approximately Gaussian Inputs." In GLOBECOM 2019 - 2019 IEEE Global Communications Conference. IEEE, 2019. http://dx.doi.org/10.1109/globecom38437.2019.9013311.

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Dytso, Alex, Natasha Devroye, and Daniela Tuninetti. "On Gaussian interference channels with mixed gaussian and discrete inputs." In 2014 IEEE International Symposium on Information Theory (ISIT). IEEE, 2014. http://dx.doi.org/10.1109/isit.2014.6874835.

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Gunduz, Deniz, and Miquel Payaro. "Gaussian two-way relay channel with arbitrary inputs." In 2010 IEEE 21st International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC 2010). IEEE, 2010. http://dx.doi.org/10.1109/pimrc.2010.5671657.

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Rodrigues, Miguel R. D., Anelia Somekh-Baruch, and Matthieu Bloch. "On Gaussian wiretap channels with M-PAM inputs." In 2010 European Wireless Conference (EW). IEEE, 2010. http://dx.doi.org/10.1109/ew.2010.5483475.

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Nielsen, Jens Brehm, Bjorn Sand Jensen, and Jan Larsen. "Pseudo inputs for pairwise learning with Gaussian processes." In 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2012. http://dx.doi.org/10.1109/mlsp.2012.6349812.

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Rodrigues, Miguel R. D., and Gil Ramos. "On multiple-input multiple-output Gaussian channels with arbitrary inputs subject to jamming." In 2009 IEEE International Symposium on Information Theory - ISIT. IEEE, 2009. http://dx.doi.org/10.1109/isit.2009.5206051.

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Laradji, Issam H., Mark Schmidt, Vladimir Pavlovic, and Minyoung Kim. "Efficient Deep Gaussian Process Models for Variable-Sized Inputs." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8851768.

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Fergie, Martin, and Aphrodite Galata. "Local Gaussian Processes for Pose Recognition from Noisy Inputs." In British Machine Vision Conference 2010. British Machine Vision Association, 2010. http://dx.doi.org/10.5244/c.24.98.

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Prakriya, Shankar, Subbarayan Pasupathy, and Dimitrios Hatzinakos. "Blind identification of nonlinear models with non-Gaussian inputs." In Photonics East '95, edited by Raghuveer M. Rao, Soheil A. Dianat, Steven W. McLaughlin, and Martin Hassner. SPIE, 1995. http://dx.doi.org/10.1117/12.228233.

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Reports on the topic "Gaussian inputs"

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Pearson, Ken, and Channing Arndt. Implementing Systematic Sensitivity Analysis Using GEMPACK. GTAP Technical Paper, November 2000. http://dx.doi.org/10.21642/gtap.tp03.

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In economic simulation, results often hinge crucially on values of key exogenous inputs (the values of the parameters of the model and the shocks applied). Computational burden has, in the past, hindered systematic investigation of the impacts of variations in these key exogenous inputs. In this document, practical methods for conducting systematic sensitivity analysis for any model implemented using the GEMPACK suite of software are documented. The procedures described here are based on GTAP Technical Paper number 2 which sets out the theory behind the Gaussian quadrature methods on which the automated procedure is based. The procedures allow modellers to obtain estimates of the means and standard deviations of any endogenous variables of their model. The model only needs to be solved a relatively modest number of times (usually only 2N times if N exogenous inputs are varying); this is considerably fewer than the number of solves required by Monte Carlo methods. The procedure documented here fully automates solving the model as often as is necessary; once the user sets it up and starts it running, no further intervention is required. The document spells out the assumptions which must be made about the distribution of the exogenous inputs for the methods described to be valid. Five examples of systematic sensitivity computations are presented and the accompanying software allows modellers to work through these examples while reading the document. This should leave readers fully prepared to analyse the sensitivity of results for any model implemented in GEMPACK.
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Tsidylo, Ivan M., Serhiy O. Semerikov, Tetiana I. Gargula, Hanna V. Solonetska, Yaroslav P. Zamora, and Andrey V. Pikilnyak. Simulation of intellectual system for evaluation of multilevel test tasks on the basis of fuzzy logic. CEUR Workshop Proceedings, June 2021. http://dx.doi.org/10.31812/123456789/4370.

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The article describes the stages of modeling an intelligent system for evaluating multilevel test tasks based on fuzzy logic in the MATLAB application package, namely the Fuzzy Logic Toolbox. The analysis of existing approaches to fuzzy assessment of test methods, their advantages and disadvantages is given. The considered methods for assessing students are presented in the general case by two methods: using fuzzy sets and corresponding membership functions; fuzzy estimation method and generalized fuzzy estimation method. In the present work, the Sugeno production model is used as the closest to the natural language. This closeness allows for closer interaction with a subject area expert and build well-understood, easily interpreted inference systems. The structure of a fuzzy system, functions and mechanisms of model building are described. The system is presented in the form of a block diagram of fuzzy logical nodes and consists of four input variables, corresponding to the levels of knowledge assimilation and one initial one. The surface of the response of a fuzzy system reflects the dependence of the final grade on the level of difficulty of the task and the degree of correctness of the task. The structure and functions of the fuzzy system are indicated. The modeled in this way intelligent system for assessing multilevel test tasks based on fuzzy logic makes it possible to take into account the fuzzy characteristics of the test: the level of difficulty of the task, which can be assessed as “easy”, “average", “above average”, “difficult”; the degree of correctness of the task, which can be assessed as “correct”, “partially correct”, “rather correct”, “incorrect”; time allotted for the execution of a test task or test, which can be assessed as “short”, “medium”, “long”, “very long”; the percentage of correctly completed tasks, which can be assessed as “small”, “medium”, “large”, “very large”; the final mark for the test, which can be assessed as “poor”, “satisfactory”, “good”, “excellent”, which are included in the assessment. This approach ensures the maximum consideration of answers to questions of all levels of complexity by formulating a base of inference rules and selection of weighting coefficients when deriving the final estimate. The robustness of the system is achieved by using Gaussian membership functions. The testing of the controller on the test sample brings the functional suitability of the developed model.
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