188 results about "Expected value of sample information" patented technology

A computer implemented method prices derivative securities (for example, options) by selecting an importance sampling (IS) distribution and combining the chosen IS distribution with stratified sampling. The process consists of the steps of choosing an importance sampling distribution and combining the chosen importance sampling with stratification or Quasi-Monte Carlo (QMC) simulation. In the first step, an importance sampling distribution is chosen. In the second step, the chosen importance sampling is combined with stratification or Quasi-Monte Carlo sequencing. The pricing of many types of securities reduces to one of estimating an expectation of a real-valued function of some random variables.

The present invention relates to a system and methodology to facilitate extraction of information from a large unstructured corpora such as from the World Wide Web and/or other unstructured sources. Information in the form of answers to questions can be automatically composed from such sources via probabilistic models and cost-benefit analyses to guide resource-intensive information-extraction procedures employed by a knowledge-based question answering system. The analyses can leverage predictions of the ultimate quality of answers generated by the system provided by Bayesian or other statistical models. Such predictions, when coupled with a utility model can provide the system with the ability to make decisions about the number of queries issued to a search engine (or engines), given the cost of queries and the expected value of query results in refining an ultimate answer. Given a preference model, information extraction actions can be taken with the highest expected utility. In this manner, the accuracy of answers to questions can be balanced with the cost of information extraction and analysis to compose the answers.

The present invention is a method and an apparatus for reward-based learning of policies for managing or controlling a system or plant. In one embodiment, a method for reward-based learning includes receiving a set of one or more exemplars, where at least two of the exemplars comprise a (state, action) pair for a system, and at least one of the exemplars includes an immediate reward responsive to a (state, action) pair. A distance metric and a distance-based function approximator estimating long-range expected value are then initialized, where the distance metric computes a distance between two (state, action) pairs, and the distance metric and function approximator are adjusted such that a Bellman error measure of the function approximator on the set of exemplars is minimized. A management policy is then derived based on the trained distance metric and function approximator.

In the Network Global Expectation Model, expectation values evaluated over the entire network are used as a multi-moment description of the required quantities of key network and network element (NE) resources and commensurate network costs. The Network Global Expectation Model naturally and analytically connects the global (network) and local (network element) views of the communication system, and thereby may be used as a tool to gain insight and very quickly provide approximate results for the preliminary evaluation and design of dynamic networks. Further, the Network Global Expectation Model may serve as a valuable guide in the areas of network element feature requirements, costs, sensitivity analyses, scaling performance, comparisons, product definition and application domains, and product and technology roadmapping. The network is arranged as a multiple tier network of nodes in order to apply the analysis methods of the Network Global Expectation Model. The analytical method is developed to include non-uniform demands on the network.

The invention provides an automatic exposure method based on an analogous column diagram, which has favourable exposure effect on various situations, such as dark background light target, target size uncertainty and the like in deep space exploration. An automatic exposure control system is a closed loop control system and mainly comprises three modules: target gray level statistics, comparative judgment with expected values and exposure time calculation; data statistics analysis is carried out by gray scale region division, gray scale value weighting average calculation and the like so as to determine exposure required by shooting. Because of having low requirement on hardware, the invention can be suitable for different hardware platforms by slightly modifying, and can balance calculation speed, logic resource use, system time sequence requirement and the like according to the practical situation so as to choose different control modes.

A statistical patent rating method and system is provided for independently assessing the relative breadth (“B”), defensibility (“D”) and commercial relevance (“R”) of individual patent assets and other intangible intellectual property assets. The invention provides new and valuable information that can be used by patent valuation experts, investment advisors, economists and others to help guide future patent investment decisions, licensing programs, patent appraisals, tax valuations, transfer pricing, economic forecasting and planning, and even mediation and/or settlement of patent litigation lawsuits. In one embodiment the invention provides a statistically-based patent rating method and system whereby relative ratings or rankings are generated using a database of patent information by identifying and comparing various characteristics of each individual patent to a statistically determined distribution of the same characteristics within a given patent population. For example, a first population of patents having a known relatively high intrinsic value or quality (e.g. successfully litigated patents) is compared to a second population of patents having a known relatively low intrinsic value or quality (e.g. unsuccessfully litigated patents). Based on a statistical comparison of the two populations, certain characteristics are identified as being more prevalent or more pronounced in one population group or the other to a statistically significant degree. Multiple such statistical comparisons are used to construct and optimize a computer model or computer algorithm that can then be used to predict and/or provide statistically-accurate probabilities of a desired value or quality being present or a future event occurring, given the identified characteristics of an individual patent or group of patents.

The invention particularly relates to a distribution network distribution method considering the uncertainty of a load prediction result, which takes the fuzzy expected value of the annual investment operating expenses of a distribution network, i.e. the fuzzy expected value of the annual investment expenses and the network loss expenses, as an optimized objective to evaluate the optimization benefits of the distribution network by the fuzzy expected value of the annual investment expenses of the distribution network and the fuzzy expected value of the annual compensation expenses. According to the known planning parameters of the distribution network and the initial net rack data of a planning region and various alternative option data, wherein a load prediction value is represented by fuzzy variables, various alternative options are represented by the variables of 0 to 1, a distribution network optimization planning model based on a fuzzy expected value model is established, and a genetic algorithm is adopted to solve. The invention can reduce the ex-post compensation expenses to a great extent and lowers losses and wastes. Meanwhile, the method considers various factors of the planning of the distribution network, wherein various factors comprise whether to establish a new transformer substation, whether to increase the capacity of the traditional transformer substation, whether to replace a circuit or establish a new circuit and the like.

The invention discloses an industrial big data driven total completion time prediction method and relates to the field of engineering application. The method includes: establishing an industrial big data analysis platform; applying an association rule mining algorithm to analyze and mine total completion time influence factors; establishing a neural network model BP; dynamically improving a weight and a threshold of the neural network model BP to acquire a dynamical neural network model DBP; applying an AIGA (adaptive immune genetic algorithm) to optimize the dynamical neural network model DBP so as to acquire a prediction model AIGA-DBP, and computing a total completion time prediction value according to the prediction model AIGA-DBP; when an error of the total completion time prediction value and a total completion time expectation value meets preset conditions, outputting the total completion time prediction value. By the method, the total completion time can be predicated accurately, the work flow of enterprises is optimized, the production efficiency of the enterprises can be improved, and the method is adaptable to various changes of the enterprise due to time lapse.

Observation and analysis based optimization of software code is disclosed. An expected value is chosen for a dynamic attribute that cannot be determined, prior to execution of the associated software code, to be guaranteed to have that expected value at runtime. An optimized version of the software code is generated, including one or more optimizations based on an assumption that the dynamic attribute will have the expected value. Non-exhaustive examples of a dynamic attribute include a variable type; a location in memory; a location in which a global object, property, or variable is stored; the contents of a global function or method; and a value of a global property or variable. A check is performed during execution of the optimized version of the software code, prior to executing the portion that has been optimized based on the assumption, to verify that the dynamic attribute has the expected value. In the event that it is determined at runtime that the dynamic attribute does not have the expected value, execution reverts to backup code that is not based on the assumption that dynamic attribute will have the expected value.

The invention discloses a bus dynamic departure scheduling optimization method based on a genetic algorithm. The method specifically comprises the steps that information of vehicles and passengers running on a single line is collected before the starting moment of a planning period; a passenger flow arrival rate function is obtained according to the real-time data and prediction, and parameters needed by model calculation are determined at the starting moment of the planning period; a scene-based dynamic bus departure scheduling robust optimization model is established by taking minimization of an expected value of total waiting time of passengers as an objective function under the condition that scenes on a single line are different; a genetic algorithm is designed for solving, and according to the probability of subjective occurrence of each scene in the preference adjustment model and the magnitude of a regret value in the model constraint, different solutions for selecting an optimal departure scheme are obtained. According to the method, the problem of dynamic bus departure scheduling under the condition that the passenger arrival rate is uncertain is solved, the waiting time of passengers is shortened, the potential risk of bus operation is reduced, and the safety and the stability of a bus system are improved.

The disclosed embodiments relate to a system and method for processing data. The system may involve a process-modeling tool adapted to define a process model, to define mapping from a resource to an activity in the process model, and to define a metric on the process model. Additionally, the system may have a designation module adapted to designate a goal and define constraints. Also included may be a process simulation engine adapted to employ the process model to simulate a process execution based on the process data according to different configurations and to produce process execution data that comprises an expected value for the metric. Further, a process improvement engine may be a component adapted to evaluate the process simulation data produced by the process simulation engine and to provide process improvement data indicative of changes in the expected value of the metric. A search tool may further be included that is adapted to search a configuration space that comprises the process improvement data to determine a prospective configuration that causes the expected value of the metric to approach the goal.

The invention discloses an equipment residual life prediction method and system. The method comprises the following steps: establishing an equipment degradation mathematical model based on a nonlineardiffusion process; obtaining an estimated value of the equipment degradation parameter under the acceleration stress as first data; calculating a degradation parameter value under normal working stress according to the first data to obtain second data; determining the distribution type of the second data through goodness-of-fit test; obtaining a posteriori distribution function and an expected value of the second data according to the distribution type of the second data; obtaining a first residual life probability density function according to the posteriori distribution function, the expected value and the equipment degradation mathematical model; correcting the first residual life probability density function according to a total probability formula to obtain a second residual life probability density function; and predicting the residual life of the equipment according to the second residual life probability density function. The prediction precision of the residual life of the equipment can be improved.

Disclosed is a power distribution network fault recovery method based on real-time contingency sets. The method includes the steps of determining a contingency severity index firstly, obtaining the expected value of influence of contingencies by combining the contingency severity index with the probability of occurrence of the contingencies, sorting the contingencies according to the severity levels according to the expected value of influence of the contingencies, screening out the contingency sets needing fault recovery analysis, updating the contingency sets within each time period, and outputting corresponding fault recovery schemes in real time. The real-time contingency sets are established, the contingencies with the small expected value of influence are omitted, only the fault recovery schemes of the contingencies with the high expected value of influence are analyzed, the number of the contingencies needing to be analyzed by on-line dynamic safety assessment is largely decreased, the calculation time for analysis of the fault recovery schemes is shortened, and real-time fault recovery plans are achieved before faults occur.

The invention discloses a seam hole reservoir body detection method and detection device. The detection method includes the steps that multiple effective attributes of a target to be detected are selected; a mathematical expectation value of a known reservoir body is output, an overdetermined equation is established for all the effective attributes in combination with linear combination of attribute values of the effective attributes, and the least square solutions of the overdetermined equation are solved to serve as attribute weighing coefficients of the effective attributes; according to the attribute weighing coefficients, multiple preferred attributes are selected from the multiple effective attributes; according to the attribute weighing coefficients and the preferred attributes, fusion of the preferred attributes is achieved with a linear weighing method. The detection method and detection device solve the technical problem that when information parameters in a single seismic attribute are used for detecting complicated seam hole type reservoir stratums, the detection is not accurate, and the detection method and detection device have the advantage that when a complex reservoir body is detected through multi-attribute information, the reservoir body can be predicted more accurately.

In the network global expectation model of the present invention, expectation values evaluated over the entire network are used as a multi-moment description of the required quantities of key network and network element (NE) resources and commensurate network costs. The network global expectation model of the present invention naturally and analytically connects the global (network) and local (network element) views of the communication system, and thereby may be used as a tool to gain insight and very quickly provide approximate results for the preliminary evaluation and design of dynamic networks. Further, the network global expectation model of the present invention may serve as a valuable guide in the areas of network element feature requirements, costs, sensitivity analyses, scaling performance, comparisons, product definition and application domains, and product and technology roadmapping.

There is provided a method for providing a function for use in detecting a presence of an exception in multidimensional data. The method comprises the steps of (a) partitioning the multidimensional data into at least a first region and a second region; (b) assigning a first region-specific function to the first region and a second region-specific function to the second region; and (c) determining a combined function from the first region-specific function and the second region-specific function. The combined function is used to calculate an expected value of the multidimensional data for distinguishing the presence of an exception.

The invention provides a control method for predicting a user behavior based on large data. The control method is used in classification analysis based on a user behavior large-data set so that a behavior expectation index I is predicted and calculated. The control method comprises the following steps: a, obtaining a user behavior variable y; b, on the basis of a function f(u, y, [phi] ... ...), obtaining characteristic parameters [beta]<1>, [beta]<2> ... [beta]

through calculation; c, on the basis of a linear combination formula u=[beta]<1>*1+[beta]<2>*2+...[beta]

*p, obtaining an expectation value u through calculation; and d, on the basis of a formula I=g(u), obtaining the behavior expectation index I through calculation. A corresponding control device is further provided. Client indexes aiming at a user risk, a purchase preference, renewal of insurance, insurance adding and other aspects can be designed through the method and device, a specific number of data is preferably selected from thousands of user data, a refined prediction model is constructed, and visual evaluation is performed on a user's future behavior trend on the basis of the prediction model.