17 results about "Gibbs sampling" patented technology

The invention relates to the technical field of automatic control, in particular to a switching positive system identification method and system and a storage medium, and aims to solve the problems that in the prior art, a model violates non-negative physical constraints, hyper-parameter estimation is prone to local optimum, calculation efficiency is low, and switching path identification precision is insufficient. The method comprises the following steps: firstly, determining non-negative priori of a system model and pulse response truncated Gaussian distribution through a preparation module; constructing a Bayesian network, and iteratively optimizing a switching path by adopting Gibbs sampling; and finally, based on regularization optimization and Gibbs sampling in combination with Monte Carlo approximation, accurately estimating the pulse response of the subsystem. According to the method, the non-negative characteristics are embedded into the whole identification process, global parameter optimization is achieved, high-dimensional integral calculation is simplified, identification precision and stability are improved, and the method is suitable for multi-scene engineering application.

This invention discloses a multi-level measurement partitioning method for tracking multiple extended targets. Addressing the low accuracy of traditional measurement partitioning when targets are close together or intersecting, this invention first establishes an extended target state representation and observation model, constructing an ET-GP-GMPHD filter. Based on predicted intensity, the measurement set is partitioned in three levels: known target measurements are assigned using k-means clustering guided by Gaussian feature contour points; new targets are identified through SOMST adaptive clustering for the remaining measurements; and multi-partition hypotheses are generated using Gibbs sampling. Finally, the partitioning results are used to update the filter and extract the target state. This invention achieves accurate measurement partitioning for close targets, significantly improving tracking accuracy and robustness.

This invention discloses a two-dimensional Gibbs sampling method based on a hierarchical Bayesian model. This method constructs a P-function that only requires calculation of two parameters based on a known hierarchical Bayesian model, then uses two-dimensional Gibbs sampling to obtain the target parameters, and finally provides an individualized drug dosage. This significantly improves computational efficiency: since only two parameters, CL and V, need to be calculated, the calculation steps are simplified, resulting in a substantial improvement in computational efficiency. No function calculation is required; the sampling step only needs to select the average values ​​of CL and V in the population as initial values ​​and substitute them into the P-function, making the sample distribution after sampling closer to the target distribution, further improving sampling efficiency.

The invention discloses a financial time series data prediction method and device and electronic equipment, and is applied to the technical field of financial data prediction. According to the financial time series data prediction method, the historical financial time series data are received and stored, the stored historical financial time series data are constructed into the financial time series data tensor, and the neighborhood set of the historical financial time series data is constructed; and finally, financial time series data prediction is carried out based on the financial time series data tensor and the neighborhood set, and a prediction result is obtained. According to the method, the potential dynamics of the financial time series data is effectively represented through the financial time series data tensor, the data driving neighborhood constructed through Gibbs sampling is used as a regularization item, the tensor hidden feature decomposition process is constrained, the hidden feature space is enabled to meet local smoothness while the global structure is kept, and the method is more efficient. Therefore, the prediction precision of the complex financial time series data is remarkably improved, and important help is provided for asset price prediction in the financial field.

The invention discloses a user load curve analysis method and system based on Bayesian nonparametric clustering and a time covariance structure, and relates to the technical field of power system data analysis, and the method comprises the steps: collecting intelligent electric meter data, and carrying out the preprocessing of the original intelligent electric meter data; calculating a covariance matrix of the preprocessed data; calculating a mahalanobis distance between the load curves based on the covariance matrix; and constructing a distance dependent Chinese restaurant process model based on mahalanobis distance, performing link allocation and clustering parameter inference in combination with Gibbs sampling, and performing adaptive clustering to obtain a user load curve analysis result. According to the method, the problem of singularity of a covariance matrix under a high-dimensional small sample is solved by adopting Leidoit-Wolf shrinkage estimation, complete Bayesian inference is realized, the clustering number is adaptively determined, and manual intervention is not needed; while high clustering precision is maintained, calculation complexity is significantly reduced, and the method is suitable for large-scale actual data.

The application belongs to the technical field of power systems, and provides a Monte Carlo-based reliability evaluation method for an IES-containing distribution network, comprising: firstly, generating a system fault state sequence covering three scenarios of a comprehensive energy system, a distribution network and simultaneous faults of both by using Markov chain Monte Carlo simulation combined with Gibbs sampling; secondly, constructing a differentiated load reduction model with tie-line power as a coupling variable for different fault types, and iteratively solving an optimal reduction scheme by using a hierarchical distributed optimization strategy and a target cascade analysis method; thirdly, aggregating and calculating expected power supply shortage, average power outage frequency and average power outage duration based on the scheme results to obtain three reliability indexes; and finally, judging the indexes by using a variance coefficient as a convergence criterion, outputting an evaluation result if the accuracy is met, or continuing iteration until convergence. The application significantly improves the accuracy and efficiency of the reliability evaluation of the IES-containing distribution network, and provides direct technical support for system configuration and dispatching strategy optimization.

This invention discloses a method and system for predicting the remaining lifespan of individual sensors based on Bayesian statistics, relating to the field of equipment health status monitoring and lifespan prediction technology. The invention provides a method comprising: establishing a general degradation model and prior distribution using historical degradation data; collecting monitoring data of a specific target sensor under real or accelerated stress in the field; updating the posterior distribution of model parameters based on Bayesian statistical inference and Gibbs sampling to generate a specific degradation model for that individual sensor; and calculating the remaining lifespan based on this specific model and a failure threshold. This invention achieves a breakthrough from "group lifespan assessment" to "accurate prediction of individual lifespan" through data-driven adaptive correction, significantly reducing over-maintenance costs and enhancing equipment operational safety.

The application discloses a construction period reservoir bank slope deformation prediction method based on a BVAR model, and is characterized in that the following steps are specifically implemented: step 1, original slope deformation monitoring data are obtained as initial samples, and the original data are subjected to ADF testing; step 2, a BVAR model is constructed; step 3, a Gibbs sampler is used to obtain BVAR model parameters to be estimated and model prediction values; and step 4, final slope deformation prediction values and prediction intervals are obtained. The application considers uncertainty factors in slope deformation monitoring based on prediction intervals, models and predicts analyzes a slope deformation monitoring sequence, has good effects, and solves the problems of less construction period slope deformation monitoring data and no environmental quantity monitoring data.

The application discloses a review usefulness prediction method considering seed information and causality, comprising the following steps: obtaining review texts and corresponding non-text data, thereby constructing a review data set D; obtaining user review preferences, thereby constructing a seed topic word distribution φ s ; constructing a Bayesian seed topic regression model based on the review data set D and the seed topic word distribution φ s ; initializing all parameters in steps S2 and S3 based on the review data set D, and performing parameter inference on a document topic distribution, a topic word distribution and a review usefulness prediction distribution by using an EM algorithm and a Gibbs sampling method. The application allows users to guide the theme discovery process by adding seed information, thereby quickly and accurately mining beneficial themes that are concerned by users, and meanwhile, the prediction accuracy is improved by jointly modeling the review texts and the review related data, so that the application can be widely applied to the fields of causality inference and linguistics.

The invention discloses an automobile competitive product and comparative attribute identification method based on large model enhancement, and the method comprises the steps: large model-driven information extraction, multi-view theme modeling and joint preference analysis, the method specifically comprises the following steps: 1) automatically extracting an automobile product and a feature description text from an evaluation copywriting by constructing a cue word guide large model, and carrying out standardized word segmentation; 2) on the basis of the de-duplicated products and the feature words, constructing a coupling LDA model, modeling automobile competitive product themes and product feature related themes, and depicting theme preference distribution of documents; and 3) inferring parameters by adopting collapse-type Gibbs sampling, calculating product and feature distribution under the theme, and mining preferences of competitive product combinations and comparative attributes of the competitive product combinations. According to the method, the problems of dependence on manually defined entities, sparse co-occurrence modes, difficulty in capturing fine-grained comparison dimensions and the like are solved, potential competitive product groups and key comparison attributes can be efficiently and accurately identified, and interpretable decision support is provided for enterprises to formulate differentiated product strategies.

The application relates to a sky-wave over-the-horizon radar coordinate registration method based on a reference source auxiliary, which comprises the following steps: firstly, acquiring prior information of ionospheric activity parameters in an estimated ionospheric region and reference source data; secondly, constructing a posterior distribution of the ionospheric activity parameters in the estimated ionospheric region under a Bayesian framework based on the prior information of the ionospheric activity parameters in the estimated ionospheric region and the reference source data; thirdly, solving a moment estimation value of the posterior distribution of the ionospheric activity parameters in the estimated ionospheric region based on a Markov chain, Gibbs sampling and importance sampling; fourthly, substituting the moment estimation value of the posterior distribution of the ionospheric activity parameters in the estimated ionospheric region into an international reference ionospheric model to obtain a posterior ionosphere; and finally, calculating an electromagnetic wave propagation path in the posterior ionosphere by using a three-dimensional ray tracing method to obtain a coordinate registration result of a to-be-positioned target. The application can significantly improve the coordinate registration precision and the sky-wave over-the-horizon radar target positioning precision.

The present application relates to the field of data processing, more particularly, the present application relates to a kind of industrial internet platform multi-source heterogeneous data fusion processing method and system, method includes: from multiple heterogeneous data sources collection multi-source text, pre-processing obtains global vocabulary;Based on industry standard classification tree, the shortest path edge number of word to each standard classification node is calculated;The distribution significance of word is calculated, and the path correlation density is obtained by combining structure attenuation factor, and then the guide coefficient of each text belonging to each standard classification node is constructed;Each standard classification node is set as a theme, and the guide coefficient is taken as priori constraint and is integrated into probability calculation in gibbs sampling process, to obtain the probability that text belongs to each theme;Through confidence threshold screening, the fusion label set of each text is output.The present application realizes the automatic alignment of multi-source heterogeneous data and industry standard classification system, improves the automation level and business availability of data fusion.

The invention relates to the technical field of computer data processing and artificial intelligence, and discloses a multi-type variable adaptive CRBM digital twinning modeling method, device and medium, the method comprises the following steps: obtaining modeling data and defining the modeling data as visible, conditional and hidden variable sets, the visible variables comprising non-standard distribution types; constructing a condition-restricted Boltzmann machine model, and directly constructing corresponding conditional probability distribution and interaction energy items according to original probability distribution characteristics for visible variables of non-standard distribution types; performing parameter updating on the model by calculating a weighted combination of a likelihood gradient and an adversarial gradient by adopting an adversarial training mechanism in which adversarial items are introduced; and using the trained model to generate digital twin data through Gibbs sampling based on a given condition input variable. According to the method, heterogeneous data can be directly processed, the original statistical characteristics of the data are reserved, and the precision of model parameter estimation and the fidelity of generated data are improved.

This invention provides a method for estimating the health status of lithium-ion batteries based on Gibbs Variational Inference Multiple Imputation (GVIMI). The method includes: constructing a lithium-ion battery degradation model with missing data; transforming the data imputation problem into a posterior approximation problem based on Gibbs variational inference; optimizing the variational lower bound using a mini-batch data approximation method; obtaining an approximate posterior distribution by maximizing the variational lower bound; and establishing a multiple imputation and health status prediction model. This invention uses a hybrid method combining Gibbs sampling and variational inference as the underlying imputator, introduces a variationally complete conditional model, and uses Gibbs variational inference to approximate the posterior distribution of the model's latent variables, optimizing the imputation model with missing data. This improves the accuracy and robustness of lithium-ion battery health status estimation in scenarios with missing data.

A digital ad-buying platform uses counterfactual-based incrementality measurement by implementing randomization and / or a correction for auction win bias to avoid the need to identify counterfactual winner types in the control group. This approach can estimate impact at the individual consumer level. Confidence levels can be determined using Gibbs sampling in the context of causal analysis in the presence of non-compliance.