1,509 research outputs found
Bayesian ensemble refinement by replica simulations and reweighting
We describe different Bayesian ensemble refinement methods, examine their
interrelation, and discuss their practical application. With ensemble
refinement, the properties of dynamic and partially disordered (bio)molecular
structures can be characterized by integrating a wide range of experimental
data, including measurements of ensemble-averaged observables. We start from a
Bayesian formulation in which the posterior is a functional that ranks
different configuration space distributions. By maximizing this posterior, we
derive an optimal Bayesian ensemble distribution. For discrete configurations,
this optimal distribution is identical to that obtained by the maximum entropy
"ensemble refinement of SAXS" (EROS) formulation. Bayesian replica ensemble
refinement enhances the sampling of relevant configurations by imposing
restraints on averages of observables in coupled replica molecular dynamics
simulations. We show that the strength of the restraint should scale linearly
with the number of replicas to ensure convergence to the optimal Bayesian
result in the limit of infinitely many replicas. In the "Bayesian inference of
ensembles" (BioEn) method, we combine the replica and EROS approaches to
accelerate the convergence. An adaptive algorithm can be used to sample
directly from the optimal ensemble, without replicas. We discuss the
incorporation of single-molecule measurements and dynamic observables such as
relaxation parameters. The theoretical analysis of different Bayesian ensemble
refinement approaches provides a basis for practical applications and a
starting point for further investigations.Comment: Paper submitted to The Journal of Chemical Physics (15 pages, 4
figures); updated references; expanded discussions of related formalisms,
error treatment, and ensemble refinement with and without replicas; appendi
A higher-order active contour model of a `gas of circles' and its application to tree crown extraction
Many image processing problems involve identifying the region in the image
domain occupied by a given entity in the scene. Automatic solution of these
problems requires models that incorporate significant prior knowledge about the
shape of the region. Many methods for including such knowledge run into
difficulties when the topology of the region is unknown a priori, for example
when the entity is composed of an unknown number of similar objects.
Higher-order active contours (HOACs) represent one method for the modelling of
non-trivial prior knowledge about shape without necessarily constraining region
topology, via the inclusion of non-local interactions between region boundary
points in the energy defining the model. The case of an unknown number of
circular objects arises in a number of domains, e.g. medical, biological,
nanotechnological, and remote sensing imagery. Regions composed of an a priori
unknown number of circles may be referred to as a `gas of circles'. In this
report, we present a HOAC model of a `gas of circles'. In order to guarantee
stable circles, we conduct a stability analysis via a functional Taylor
expansion of the HOAC energy around a circular shape. This analysis fixes one
of the model parameters in terms of the others and constrains the rest. In
conjunction with a suitable likelihood energy, we apply the model to the
extraction of tree crowns from aerial imagery, and show that the new model
outperforms other techniques
Optimal design of dilution experiments under volume constraints
The paper develops methods to construct a one-stage optimal design of
dilution experiments under the total available volume constraint typical for
bio-medical applications. We consider various design criteria based on the
Fisher information both is Bayesian and non-Bayasian settings and show that the
optimal design is typically one-atomic meaning that all the dilutions should be
of the same size. The main tool is variational analysis of functions of a
measure and the corresponding steepest descent type numerical methods. Our
approach is generic in the sense that it allows for inclusion of additional
constraints and cost components, like the cost of materials and of the
experiment itself.Comment: 29 pages, 10 figure
- …