Abstract: The invention relates to sensitivity analysis of variables influencing a combustion process. A trainable, statistical model is trained in such a way that it describes the combustion process in the combustion chamber. The trained statistical model is used to determine the influence of the variables on said combustion process in the combustion chamber.

Abstract: The invention relates to sensitivity analysis of variables influencing a combustion process. A trainable, statistical model is trained in such a way that it describes the combustion process in the combustion chamber. The trained statistical model is used to determine the influence of the variables on said combustion process in the combustion chamber.

Abstract: The invention relates to a data bank interrogation system, wherein two or more data bank tables are linked by means of a common key or several keys which are respectively common to at least two data bank tables. In an analysis query and a selection of data sets in the first data bank, a selection of data sets is determined in the second data bank corresponding to the selection according to the common key and the analysis query is answered using the thus selected data sets in the second data bank.

Abstract: The invention relates to a data base query system which is characterized in that once the database query is drawn up, a compressed image of the database to be queried is queried in accordance with the database query. Depending on the result of the query of the compressed image an inspection is made whether the result is sufficient and if the result is not sufficient, the database itself is queried in accordance with the database query.

Abstract: A method for performing an automatic software-driven statistical evaluation of a large amount of data to be assigned to statistical variables in a database contained in at least one cluster. The method is characterized by using a statistical model to model an approximate description of a relative frequency of the state or states of the statistical variables and a statistical dependencies between the state or states, and then determining the approximate relative frequency of the state or states of the statistical variables and the approximate relative frequency belonging to a predetermined relative frequency of the state or states of the statistical variables and an expected value of the state or states of the statistical variables dependent thereon.

Abstract: A method for performing an automatic software-driven statistical evaluation of a large amount of data to be assigned to statistical variables in a database contained in at least one cluster. The method is characterized by using a statistical model to model an approximate description of a relative frequency of the state or states of the statistical variables and a statistical dependencies between the state or states, and then determining the approximate relative frequency of the state or states of the statistical variables and the approximate relative frequency belonging to a predetermined relative frequency of the state or states of the statistical variables and an expected value of the state or states of the statistical variables dependent thereon.

Abstract: The invention relates to analysis of user data organized according to a database structure, such as customer data or product data in a company.

Abstract: The invention relates to a method and a system for detecting at least one partial model of a model pertaining to a system. A state of the system is described by state variables. At least on e of the state variables is a discrete state variable. Several value sets of the state variables are detected. A probability distribution for the state variables is detected by using the sets. The partial model of the system is detected using the sets and the probability distribution of the state variables and a statistical learning method. The partial model describes the system under the condition of the probability distribution for the state variables.

Abstract: 09 A first statistical image is formed for the first database whereby representing statistical correlations of the data elements contained in the first database. Afterwards, the first statistical image is stored in a server computer and transmitted from this server computer over a communications network to a client computer. The received first statistical image is processed by the client computer.

Abstract: For inference in a statistical model, or in a clustering model the formation of the result bitches formed from the terms of the association function or a conditional probability tables, of using the normal procedures, but as soon as the first zero occurs in the associated factors or a weight of zero has been determined for a cluster in the first steps, enabling the further calculation of the a posteriori weight to be aborted. In the case in which in an iterative learning process (e.g. an EM learning process) a cluster for a specific data point is assigned a weight of zero, this cluster will also be given the weight of zero for this data point for all further learning steps and therefore must also no longer be taken into consideration in all further learning steps. Useful data structures for buffering clusters or states of a variable which are still allowed from one learning step to the next are specified. This guarantees a meaningful removal of processing of irrelevant parameters and data.

Abstract: The invention relates to a method for the automatic, software-driven statistical evaluation of large amounts of data that is to be assigned to statistical variables in a database. Said method is characterised in that a statistical model, which approximately describes the relative frequencies of the states of the variables and the statistical dependencies between said states, is learnt and is used to determine the approximate relative frequencies of states of the variables, in addition to the approximate relative frequencies belonging to predeterminable relative frequencies of states of the variables and expectation values of the states of variables dependent thereon.

Abstract: The invention relates to a method and an arrangement for detecting at least one partial model of a model pertaining to a system. A state of the system is described by state variables. At least one of said state variables is a discrete state variable. Several value sets of the state variables are detected. A probability distribution for the state variables is detected by means of said sets. The partial model of the system is detected using the sets and the probability distribution of the state variables and a statistical learning method. The partial model describes the system under the condition of the probability distribution for the state variables.

Abstract: In a method for tranining a neural network with the non-deterministic behavior of a technical system, weightings for the neurons of the neural network are set during the training using a cost function. The cost function evaluates a beneficial system behavior of the technical system to be modeled, and thereby intensifies or increases the weighting settings which contribute to the beneficial system behavior, and attenuates or minimizes weightings which produce a non-beneficial behavior. Arbitrary or random disturbances are generated by disturbing the manipulated variable with noise having a known noise distribution, these random disturbances significantly faciliating the mathematical processing of the weightings which are set, because the terms required for that purpose are simplified. The correct weighting setting for the neural network is thus found on the basis of a statistical method and the application of a cost function to the values emitted by the technical system or its model.