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Robustly Parameterised Higher-Order Probabilistic Models
2016
International Conference on Concurrency Theory
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We present a method for constructing robustly parameterised families of higher-order probabilistic models. ...
Naturality ensures that parameterised models are invariant by change of granularity -ie that parameterisations are intrinsic. ...
The characterisation of objects of Pol cz can be formulated as follows:
23:4 Robustly Parameterised Higher-Order Probabilistic Models The family of zero-dimensionalisations of a space X indexed by all ...
doi:10.4230/lipics.concur.2016.23
dblp:conf/concur/DahlqvistDG16
fatcat:x4y5hb4npbd2xdtwilpowix2nq
Robustly Parameterised Higher-Order Probabilistic Models *
unpublished
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We present a method for constructing robustly parameterised families of higher-order probab-ilistic models. ...
Continuity ensures that models are robust with respect to their parameterisation. ...
The characterisation of objects of Pol cz can be formulated as follows:
23:4 Robustly Parameterised Higher-Order Probabilistic Models The family of zero-dimensionalisations of a space X indexed by all ...
fatcat:et2s4hxdtzaejebtkupxtqi4ji
CAD based shape optimization for gas turbine component design
2009
Structural And Multidisciplinary Optimization
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This shape change can be realised by modifying the parameter values of a suitably parameterised Computer Aided Design (CAD) model. ...
In order to improve product characteristics, engineering design makes increasing use of Robust Design and Multidisciplinary Design Optimisation. ...
The first step is the construction of a parameterised CAD model. ...
doi:10.1007/s00158-009-0442-9
fatcat:zn4kwfseuzhmjilsodf7gtru7i
Hyperresolution information and hyperresolution ignorance in modelling the hydrology of the land surface
2014
Science China. Earth Sciences
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There is a strong drive towards hyperresolution earth system models in order to resolve finer scales of motion in the atmosphere. ...
This paper discusses these issues in more detail with specific reference to land surface parameterisations and flood inundation models. ...
Introduction It is a general expectation in earth system science modelling that we will continue to move to higher and higher resolution coupled models, with so called hyperresolution models (on the order ...
doi:10.1007/s11430-014-5003-4
fatcat:h2oib5lkbjgltm4dxt2v2nug34
Consensus between Pipelines in Structural Brain Networks
2014
PLoS ONE
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We aimed to quantify network similarity and identify the core connections emerging most robustly in both pipelines. ...
This study demonstrates the utility of applying multiple structural network reconstrution pipelines to diffusion data in order to identify the most important connections for further study. ...
Both reconstructions had similar capabilities but varied with respect to the details of the cortical parcellation, registration and probabilistic fiber model method. ...
doi:10.1371/journal.pone.0111262
pmid:25356977
pmcid:PMC4214749
fatcat:hllkwt27yfacnapctjz46rt7gy
Multi-view Probabilistic Segmentation of Pome Fruit with a Low-Cost RGB-D Camera
[chapter]
2017
Advances in Intelligent Systems and Computing
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Furthermore, the model provides probabilistic reconstruction of the entire apple which can be used for better grasping of the fruit. ...
In order to perform this task, robots should be able to recognize and segment fruit in their perceptual environment. ...
In order to achieve better results in the probabilistic segmentation we perform registration of point clouds taken from multiple points of view. ...
doi:10.1007/978-3-319-70836-2_27
fatcat:2gynn2grubgpvo7tsm7vnv4loa
Modelling Non-stationary and Non-separable Spatio-Temporal Changes in Neurodegeneration via Gaussian Process Convolution
[chapter]
2015
Lecture Notes in Computer Science
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The proposed model is parameterised by a sparse set of control points independently identified by specific spatial and temporal parameters. ...
These assumptions are often made in order to lead to computationally tractable approaches to longitudinal modelling, but inevitably lead to an oversimplification of the complex spatial and temporal dynamics ...
In fact, flexible modelling instruments are required in order to robustly capture meaningful pathological accelerations specific to sensitive brain regions. ...
doi:10.1007/978-3-319-27929-9_4
fatcat:df2cpqnwoberleedwktflamlve
Social Motorics – Towards an Embodied Basis of Social Human-Robot Interaction
[chapter]
2009
Cognitive Systems Monographs
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Based on an unified sensorimotor representation, it integrates hierarchical motor knowledge structures, probabilistic forward models for predicting observations, and inverse models for motor learning. ...
In this paper we present a biologically-inspired model for social behavior recognition and generation. ...
With a focus on hand-arm gestures, we describe a probabilistic model that exploits hierarchical motor structures with forward and inverse models in order to allow resonance-based processing of social behavior ...
doi:10.1007/978-3-642-10403-9_20
fatcat:oemljdrnovdzpkhjxm35hevbd4
Stable Implementation of Probabilistic ODE Solvers
[article]
2020
arXiv
pre-print
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Using all three techniques enables numerical computation of probabilistic solutions of ODEs with algorithms of order up to 11, as demonstrated on a set of challenging test problems. ...
The resulting rapid convergence is shown to be competitive to high-order, state-of-the-art, classical methods. ...
The underlying ODE is the Lotka-Volterra model in the parameterisation from the experiments below. ...
arXiv:2012.10106v1
fatcat:nsrhmvxtynfyddufzmmrd4h3qa
A Probabilistic Background Model for Tracking
[chapter]
2000
Lecture Notes in Computer Science
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A new probabilistic background model based on a Hidden Markov Model is presented. The hidden states of the model enable discrimination between foreground, background and shadow. ...
This model functions as a low level process for a car tracker. A particle filter is employed as a stochastic filter for the car tracker. ...
In order to track the cars robustly it is necessary to detect the shadows as well as the cars.
Fig. 2 . 2 Intensity histograms of the different regions. ...
doi:10.1007/3-540-45053-x_22
fatcat:sg6n53yu7ndhdb3bunxgmmxwcy
MIXED PROBABILITY MODELS FOR ALEATORIC UNCERTAINTY ESTIMATION IN THE CONTEXT OF DENSE STEREO MATCHING
2021
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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To allow a fair comparison, we adapt a common neural network architecture to investigate the effects of the different uncertainty models. ...
To overcome this limitation, we propose two novel mixed probability models consisting of Laplacian and Uniform distributions for the task of aleatoric uncertainty estimation. ...
in Section 5.2, in order to verify the validity of the proposed uncertainty models. ...
doi:10.5194/isprs-annals-v-2-2021-17-2021
fatcat:5fiqd73nq5erte536gcujp65y4
On the probabilistic epipolar geometry
2008
Image and Vision Computing
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We will thus have a point-probability-density relation which can be understood as a probabilistic form of the epipolar constraint; it also approaches the true point-line relation as the number of training ...
Derivation of the Probabilistic Epipolar Constraint In this section, we will further develop the considerations presented in Section 2 and derive the probabilistic epipolar constraint. ...
The fundamental matrix and its covariance matrix were automatically robustly estimated from point correspondences as proposed in [1, 2] . ...
doi:10.1016/j.imavis.2006.12.002
fatcat:h53r2i3xufay3ebxefnecnyvmu
On the Probabilistic Epipolar Geometry
2004
Procedings of the British Machine Vision Conference 2004
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We will thus have a point-probability-density relation which can be understood as a probabilistic form of the epipolar constraint; it also approaches the true point-line relation as the number of training ...
Derivation of the Probabilistic Epipolar Constraint In this section, we will further develop the considerations presented in Section 2 and derive the probabilistic epipolar constraint. ...
The fundamental matrix and its covariance matrix were automatically robustly estimated from point correspondences as proposed in [1, 2] . ...
doi:10.5244/c.18.13
dblp:conf/bmvc/Brandt04
fatcat:5lim6x24o5buddsnlubjmpkfv4
Predicting the causative pathogen among children with pneumonia using a causal Bayesian network
[article]
2022
medRxiv
pre-print
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both domain expert knowledge and numerical data.MethodsWe used domain expert knowledge and data in combination and iteratively, to construct, parameterise and validate a causal BN to predict causative ...
Causal Bayesian networks (BNs) serve as powerful tools for this problem as they provide clear maps of probabilistic relationships between variables and produce results in an explainable way by incoporating ...
the model as a series of conditional probability tables (CPTs), i.e., parameterisation. ...
doi:10.1101/2022.07.01.22277170
fatcat:acaxpsyuzvfrdksbpupyuah6wm
Negative feedback may suppress variation to improve collective foraging performance
[article]
2020
bioRxiv
pre-print
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Introduction Collectively-foraging social insects need to use feedback mechanisms in order to robustly and efficiently satisfy the nutritional requirements of the colony. ...
The model with negative feedback, however, requires a heuristic individual parameterisation based on site qualities, which we perform numerically (see Supplementary Text). ...
This is supplemented by a notebook for MuMoT [42] , an open-source tool for multiscale modelling, which reproduces similar results to those presented herein. ...
doi:10.1101/2020.04.21.053074
fatcat:keqcwonsozdenbkm4s65uqx3qe
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