Relatively-Integrated Ship Navigation by H¥ Fusion Filters

For the system with unknown statistical property noises, the property that the energies of the system noise and the observation noise are limited is utilized in this paper. On this basis, two novel fusion algorithms are proposed for ship integrated navigation with the relative navigation information, broadcasted by the Automatic Identification Systems (AISs) in the adjacent ships. Firstly, an H∞ fusion filtering algorithm is given to deal with the navigation observation messages, under the centralized fusion framework. The integrated navigation method based on this algorithm cannot deal with the asynchronous navigation messages in real time. Therefore, a sequential H∞ fusion filtering algorithm is also given to sequentially deal with the asynchronous navigation messages, secondly. Finally, a computer simulation is employed to illustrate the validity and feasibility of the sequential method.

Relatively Integrated Ship Navigation by H∞ Fusion Filters

For the system with unknown statistical property noises, the property that the energies of the system noise and the observation noise are limited is utilized in this paper. On this basis, two novel fusion algorithms are proposed for ship integrated navigation with the relative navigation information, broadcasted by the Automatic Identification Systems (AISs) in the adjacent ships. Firstly, an H∞ fusion filtering algorithm is given to deal with the navigation observation messages, under the centralized fusion framework. The integrated navigation method based on this algorithm cannot deal with the asynchronous navigation messages in real time. Therefore, a sequential H∞ fusion filtering algorithm is also given to sequentially deal with the asynchronous navigation messages, secondly. Finally, a computer simulation is employed to illustrate the validity and feasibility of the sequential method.

P-sort: an open-source software for cerebellar neurophysiology

Algorithms that perform spike sorting depend on waveforms to cluster spikes. However, a cerebellar Purkinje-cell produces two types of spikes; simple and complex spikes. A complex spike coincides with the suppression of generating simple spikes. Here, we recorded neurophysiological data from three species and developed a spike analysis software named P-sort that relies on this statistical property to improve both the detection and the attribution of simple and complex spikes in the cerebellum.

Statistical learning reflects inferences about unique predictive relations

The mind adeptly registers statistical regularities in experience, often incidentally and implicitly. We used a visual statistical learning paradigm to study what kinds of statistics it spontaneously computes in such conditions. We found that participants’ learning of pairwise predictive relations was best explained by an inferentially sophisticated quantity, deltaP, which reflects whether a high conditional probability between an event pair is unique. We showed that uniqueness can be reduced by either a strong competing predictor or an overall high base rate of the outcome. Both can result from normalization: if predictors of the same effect trade off, a predictor must raise the probability of the effect more than the others to be effective. Adding normalization to the Rescorla-Wagner learning model captures these results. We argue that the uniqueness of a relation is an intrinsically important statistical property that governs learning without incentive or deliberation.

Issues Relating to the Creation of a Central Database to Support Statistical Property Valuations

The valuation of real estate collateral is a long-established area of the lending process that is currently undergoing increasingly dynamic development and in which the use of statistical valuation methods is becoming more and more common instead of on-site valuations. The legal conditions for this have been created by amendments to European and national legislation in the past year, but for the method to be truly widely used and operational and to ensure the accuracy of the resulting valuations, access to detailed, accurate, up-to-date and regularly checked data on real estate must be also created. As the databases currently available for Hungarian real estate are very fragmented, in our study, we propose to create a central database that would provide a uniform, up-to-date set of data, by harmonising the existing separate databases. Such a database would help create a level playing field in the market and automate data transfer in a cost-effective, fast and reliable manner. This would greatly facilitate the uptake of statistical valuation methods, supporting the further spread of digitalisation, increasing banking competition, speeding up administration and reducing the cost of lending for all parties.