Patents
Literature
2 results about "Set estimation" patented technology
Efficacy Topic
Property
Owner
Technical Advancement
Application Domain
Technology Topic
Technology Field Word
Patent Country/Region
Patent Type
Patent Status
Application Year
Inventor
In statistics, a random vector x is classically represented by a probability density function. In a set-membership approach or set estimation, x is represented by a set X to which x is assumed to belong. This means that the support of the probability distribution function of x is included inside X. On the one hand, representing random vectors by sets makes it possible to provide fewer assumptions on the random variables (such as independence) and dealing with nonlinearities is easier. On the other hand, a probability distribution function provides a more accurate information than a set enclosing its support.
Next state prediction using likelihood estimates
Example implementations related to next state prediction and action selection are disclosed. In an example, an initial state is estimated based on a set of priors including at least one engagement opportunity. A diminishing effect of the at least one engagement opportunity is determined using a non-linear transformation and a set of likelihood estimates for the initial state is generated using a Bayesian steady state filter model that receives the initial state and the diminishing effect of the at least one engagement opportunity. A next state is predicted based on the initial state and the set of likelihood estimates.
Owner:WALMART APOLLO LLC
Knowledge-ability-competence triple map-based new engineering course teaching evaluation method
The application discloses a kind of new engineering course teaching evaluation methods based on knowledge-ability-quality triple spectrum, belong to wisdom education and intelligent control technical field.Data of three dimensions of student knowledge, ability and quality are collected to construct a unified state vector, and the dynamic evolution of the learning process is described by combining the state update and prediction model of residual network;Multi-cell filtering method is used for state set estimation, and in the prediction step, linear propagation and residual local linearization are combined, and the safety and reliability of state interval estimation are guaranteed by using Lipschitz upper bound to circumscribe the nonlinear residual;In the update step, the set is tightened by the intersection of the prediction strip and the generator contraction, the coverage calibration based on quantile is introduced to ensure the confidence level of set estimation.The application can dynamically, stably and explainably estimate the learning state of students, and realize accurate and efficient course adjustment optimization, with good application prospect and popularization value.
Owner:JIANGNAN UNIV