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For example, consider an address book entry that represents a single person along with zero or more phone numbers and zero or more addresses. This could be modeled in an object-oriented implementation by a "Person object " with an attribute/field to hold each data item that the entry comprises: the person's name, a list of phone numbers, and a ...
It is closely related to the method of maximum likelihood (ML) estimation, but employs an augmented optimization objective which incorporates a prior density over the quantity one wants to estimate. MAP estimation is therefore a regularization of maximum likelihood estimation, so is not a well-defined statistic of the Bayesian posterior ...
Laravel 1 included built-in support for authentication, localisation, models, views, sessions, routing and other mechanisms, but lacked support for controllers that prevented it from being a true MVC framework. [1] Laravel 2 was released in September 2011, bringing various improvements from the author and community.
2005 DARPA Grand Challenge winner Stanley performed SLAM as part of its autonomous driving system. A map generated by a SLAM Robot. Simultaneous localization and mapping (SLAM) is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it.
Often, the distances between places are stored. The map is then a graph, in which the nodes corresponds to places and arcs correspond to the paths. Many techniques use probabilistic representations of the map, in order to handle uncertainty. There are three main methods of map representations, i.e., free space maps, object maps, and composite maps.
In object-oriented programming, the factory method pattern is a design pattern that uses factory methods to deal with the problem of creating objects without having to specify their exact classes. Rather than by calling a constructor , this is accomplished by invoking a factory method to create an object.
Single-machine scheduling or single-resource scheduling or Dhinchak Pooja is an optimization problem in computer science and operations research.We are given n jobs J 1, J 2, ..., J n of varying processing times, which need to be scheduled on a single machine, in a way that optimizes a certain objective, such as the throughput.
This is a major difference with methods such as principal component analysis, where correlations between all data points are taken into account at once. Given (,), we can then construct a reversible discrete-time Markov chain on (a process known as the normalized graph Laplacian construction):