32 results about "Maximum entropy probability distribution" patented technology

An apparatus generates a statistical class sequence model called A class bi-multigram model from input training strings of discrete-valued units, where bigram dependencies are assumed between adjacent variable length sequences of maximum length N units, and where class labels are assigned to the sequences. The number of times all sequences of units occur are counted, as well as the number of times all pairs of sequences of units co-occur in the input training strings. An initial bigram probability distribution of all the pairs of sequences is computed as the number of times the two sequences co-occur, divided by the number of times the first sequence occurs in the input training string. Then, the input sequences are classified into a pre-specified desired number of classes. Further, an estimate of the bigram probability distribution of the sequences is calculated by using an EM algorithm to maximize the likelihood of the input training string computed with the input probability distributions. The above processes are then iteratively performed to generate statistical class sequence model.

Circuits that solve stochastic problems and techniques for operating them. These natively stochastic circuits may produce samples from probability distributions of interest for particular stochastic problems, and may be combined together in any suitable way to yield potential solutions to stochastic problems. In some implementations, the stochastic circuits may generate samples from conditional probability distributions conditioned on input data provided to the stochastic circuits. The circuits may be constructed from multiple interconnected stochastic subcircuits such that a circuit may produce a sample from a joint probability distribution, or from a marginal distribution of a joint distribution. These circuits may be used to implement stochastic sampling algorithms to solve stochastic processes, and may include stochastic subcircuits that operate concurrently.

The invention provides an air quality fast traceability forecast method based on a Lagrangian transportation model. The method comprises the steps that a mesoscale weather forecasting mode is used to simulate the meteorological field of a simulated area; according to the meteorological field requirements of the Lagrangian transportation model, key elements, which are output by a meteorological field simulation system, of the meteorological field are extracted, and the data format is converted; basic parameters of the Lagrangian transportation model are set; the space-time probability distribution of released particles in the meteorological field is calculated through the Lagrangian transportation model, and a particle probability distribution database is built; the space-time probability distribution of the released particles and a pollution source list in the simulated area are combined to calculate the potential source contribution distribution of pollutants; and finally, statistical simulation is carried out on the calculated pollutant concentration and the contribution of each industry to the pollutant concentration in the pollutant list. According to the invention, the air pollution source of an area and a city can be analyzed and forecasted, and the identification of the key area of pollution contribution and quick response demonstration of and pollution emission reduction measures can be realized.

The invention discloses a method for determining whether passages issued by network users have copy relations or not. The method includes: acquiring time probability distribution obeyed by time intervals of two passages with clear forwarding relations; deducing time probability distribution obeyed by time intervals of two passages with copy relations according to the acquired time probability distribution; setting a range which needs to be satisfied by time intervals of the two passages with copy relations according to the deduced time probability distribution; for optional two passages whose time intervals are in the range, calculation the similarity of the two; and determining whether the two passages have copy relations according to the similarity.

The invention discloses an electric power system static safety assessment method based on a probabilistic tide. Statistical information of the random quantity in a system is obtained according to historical data; mutual conversion between a relevant non-normal random variable and an independent normal distribution random variable is achieved by improving Nataf conversion. By means of the electric power system static safety assessment method based on the probabilistic tide, arbitrary probability distribution of many random factors in the electric power system can be coped with, the correlation among the random factors is considered, the processing difficulty of the correlation is reduced, and the electric power system static safety assessment method is suitable for assessing the influence of all kinds of uncertain factors in the electric power system on the static safety of the electric power system.

The invention discloses a deep network intelligent investment system data analysis method integrating an attention mechanism. The method includes the following steps that: step 1, financial fields called by a sufficient quantity of local devices are obtained from a financial website and a stock database, the financial fields are filtered and integrated, so that a field X can be obtained; step 2, the field X is inputted into an encoder module, wherein the encoder module is composed of a long-term and short-term memory network, and encodes the X; step 3, an encoded field X vector obtains an attention allocation probability distribution value within a probability distribution value interval through an attention allocation module; step 4, the long-term and short-term memory network in the decoder generates price predictions on the basis of a field code containing attention probability distribution and historical information that has been generated before; step 5, a trained deep network outputs the prediction result of a certain trading day, and compares the prediction result of the trading day with a set threshold value, and the risks of financial products are determined; and step 6, appropriate financial products are screened according to user funds, and an optimal investment portfolio is configured.

The invention relates to a fault detection method based on a correlation probability model. The method comprises the following steps: selecting a to-be-detected parameter from a plurality of telemetryparameters; dividing the to-be-detected parameters into a plurality of working units in time; determining a correlation coefficient between every two parameters in the plurality of telemetry parameters in each working unit so as to determine a correlation coefficient vector of the working unit; establishing a correlation probability model for the plurality of working units to determine multivariate probability distribution of the plurality of working units; carrying out dimension reduction on the multivariate probability distribution through principal component analysis (PCA) to obtain unaryprobability distribution; and performing anomaly detection on the data of the unary probability distribution to determine an abnormal to-be-detected parameter. According to the invention, parameter abnormity can be rapidly detected in the early stage of satellite faults, so that health state monitoring and system maintenance of satellites are facilitated.

The invention discloses a nano porous media modeling method and system with the relative density and the edge diameter controllable. The method includes the following steps that S100, grid division isconducted on a cube model in the three-dimensional direction, and a three-dimensional grid model is obtained; S200, a finite difference method is used for obtaining the three-dimensional grid model with a three-dimensional bicontinuous porous structure; S300, the jump function is used for calculating the relative density of the three-dimensional grid model at the moment; S400, a random cut line is generated in a grid point of each edge of the three-dimensional grid model, a binormal distribution function is used for fitting the length probability density distribution of the random cut line, and the length corresponding to an extreme point with the maximum length probability distribution is the average edge diameter; S500, the steps S100-S300 are iteratively executed until the relative density and the average edge diameter of the three-dimensional grid model meet the preset requirements. By adopting the method, the nano porous media modeling method and system with the relative densityand the edge diameter controllable can be obtained, and the obtained model can be used for unidirectional stretching molecular dynamics simulation.

Systems and methods are described for simultaneously deriving an effective x-sigma corner for multiple, different circuit and/or process metrics for a semiconductor device. The result is an effective sigma that is representative of design intent. Some implementations account for covariance, and use joint probability as the criteria for the effective x-sigma corner (e.g., as opposed to a unique sigma level of each individual metric). Analysis results for each metric can be transformed to metric distributions in a common distribution framework, and a correlation matrix can be calculated. The transformed metric distributions can be input to a joint probability distribution set to achieve a target joint sigma level. The joint probability distribution and correlation matrix values can be used to back-calculate scaled x-sigma corners for each metric distribution. Simulation of the device can be performed at one or more of the scaled x-sigma corners.

The invention discloses an SAR image segmentation method based on a triple discrimination random field. The defect that in the prior art, when a TMRF model of a triple Markov random field is adopted for capturing statistical correlation of observation data, the operating distance must depend on an adopted neighbourhood system is overcome. The SAR image segmentation method includes the following steps of (1) inputting an SAR image to be segmented, (2) extracting features and initializing, (3) obtaining joint probability distribution, (4) obtaining posterior marginal probability distribution, (5) segmenting the SAR image, (6) estimating model parameters and (7) outputting the segmented image. Through introducing related information between non-stationary information and pixel point observation values of the SAR image, consistency of segmented areas is kept, and marginal accuracy of a segmentation result is improved.

The invention discloses a method for extracting reservoir dispatching function based on joint probability distribution. By collecting reservoir basic data and long series of measured runoff data, theoptimal dispatching process sample series are obtained by deterministic optimal dispatching of reservoirs. The marginal probability distribution functions of decision variables and state variables aredetermined, and the joint probability distribution functions of decision variables and state variables are constructed by using Copula function, then the conditional probability distribution functions of decision variables are deduced when the state variables are given, and the reservoir operation functions are extracted and the uncertainties are analyzed. The reservoir dispatching function extracted by the invention can accurately capture the inherent non-linear and heteroscedasticity correlation structure of the decision variable and the state variable, and the calculated confidence interval estimation value can quantitatively evaluate the uncertainty of the reservoir dispatching, thereby providing a beneficial reference basis for the decision risk analysis.

The present invention provides a disturbance source tracing method adapted for tracing a disturbance source of a system including a plurality of candidate nodes by a computing apparatus. In this method, the computing apparatus obtains a plurality of topology information by analyzing the system using a plurality of process analyzing methods respectively. The topology information records causalitiesbetween the candidate nodes of the system. The computing apparatus applies each causality of the topology information to a probability distribution algorithm, and calculates a stationary probabilitydistribution of each of the candidate nodes. The computing apparatus also synthesizes the stationary probability distributions calculated with respect to each process analyzing method to calculate a probability distribution of each of the candidate nodes being the disturbance source and find the disturbance source causing the disturbance in the system from lots of oscillation circuits.

The invention discloses a regional energy system optimization method based on type II fuzzy chance constraint, belonging to the field of energy system optimization dispatching. The method comprises the steps of: A, building an optimization model with the minimum regional energy system cost as the objective and with various kinds of constrains as constraint conditions; B, constructing type II fuzzyparameters according to the method for determining weights and the parameter possibility distribution, constructing random parameter according to the parameter probability distribution, and converting the type II fuzzy parameters and the random parameters into deterministic parameters through a type II fuzzy reduction method based on critical values and an opportunity constraint method, and solving the model; C, choosing different default probabilities of the energy system and substituting the probabilities into the model for solving, and obtaining the relationship between system default probability and system cost, energy purchase, energy supply and pollutant emission. The invention can optimize the regional energy system under the condition of multiple uncertainties, and provides an optimal energy purchase, energy supply and pollutant emission scheme.

The invention discloses an uncertain power flow calculation method based on hybrid random and interval variables, which relates to the technical field of power system voltage stabilization. The methodincludes the following steps: natural factors such as wind speed and light intensity are expressed as interval variables, and a wind power generation interval model, a photovoltaic power generation interval model and a node load probability model are constructed; the wind power generation interval model, the photovoltaic power generation interval model and the node load probability model are introduced into a power flow equation, and a power flow model based on hybrid random and interval variables is established; and calculation is performed on the power flow model based on hybrid random andinterval variables by adopting a double-layer Monte Carlo method to obtain the maximum and minimum cumulative probability distribution of power flow, and thus, the probability interval of power flow exceeding the limit is determined. By using the method of the invention, the maximum cumulative probability distribution and the minimum cumulative probability distribution of power flow can be obtained, and thus, the probability interval of power flow exceeding the limit can be determined.

The invention proposes a ''dessert'' probability direct estimation method and a ''dessert'' probability direct estimation system based on prestack inversion. The ''dessert'' probability direct estimation method comprises the steps of: utilizing well logging data to obtain a joint probability distribution among inversion parameters, that is, the joint probability distribution of a longitudinal wavemodulus and a density; using a probability estimation method to obtain an initial ''dessert'' phase probability; calculating a conditional probability distribution of a ''dessert'' sensitive parameter longitudinal wave modulus and a shear modulus, and sampling from the conditional probability distribution to obtain new longitudinal wave modulus and shear modulus models; calculating a conditionalprobability distribution of the density according to the new longitudinal wave modulus model through combining the longitudinal wave modulus with the density, and sampling from the conditional probability distribution to obtain a new density model; and calculating a new ''dessert'' phase probability, and utilizing an acceptance probability formula to determine whether to update the inversion parameters and the ''dessert'' phase probability model. The ''dessert'' probability direct estimation method and the ''dessert'' probability direct estimation system can obtain a series of samples of the related inversion parameters and the posterior probability distribution of the ''dessert'' phase probability through continuous iteration, and realize the direct estimation of the ''dessert'' phase probability.

Systems, methods and computer program products for analyzing stochastic characteristics, including one or more subsystem for forecasting financial statements and financial ratios' probability distributions and configured for at least one of: analyzing insurance contracts and portfolios without simplifying contract and product terms and conditions; studying and illustrating financial statement probability distributions; analyzing insurance portfolios by creating net asset value distributions thereof without deterministic assumptions; modeling to support asset and liability management, wherein both assets and liabilities are simulated simultaneously and decisions are based on joint probability distributions thereof; and modeling to study effects of model specification changes by implementing new model definitions and by rerunning the model with constant random number generator seed.

The invention discloses a data difference analysis method and system based on probability density estimation, and belongs to the field of data analysis. The method comprises the following steps of firstly, establishing a data set, wherein the data in the data set changes; estimating the joint probability of the data before and after the change by using a probability density estimation method; selecting an optimal window width according to a maximum likelihood method, for different window widths, taking any one point in the data set every time, constructing the joint probability distribution by using the rest points in the data set, calculating a joint probability density value of the any one point on the joint probability distribution, obtaining a product of a plurality of joint probability density values as a likelihood value, and enabling the window width with the maximum likelihood value to be the optimal window width; and obtaining the data joint probability density distribution before and after change through the probability density estimation method according to the optimal window width, and analyzing the data difference. According to the method, the significance degree of each piece of data can be obtained without being limited by the data distribution and is used for discovering the remarkably changed data.