A learning apparatus and method for learning a model corresponding to time-series input data, comprising: acquire the time-series input data; supply a plurality of input nodes of the model with a plurality of input values corresponding to input data at one time point in the time-series input data; store values of hidden nodes; compute a conditional probability of each input value at the one time point on a condition that an input data sequence has occurred, based on the input data sequence before the one time point in the time-series input data, on the stored values of hidden nodes, and on weight parameters; and perform a learning process that further increases a conditional probability of input data occurring at the one time point on the condition that the input data sequence has occurred, by adjusting the weight parameters.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A learning apparatus for learning a model corresponding to time-series input data, comprising: a processor; and one or more computer readable mediums collectively including instructions that, when executed by the processor, cause the processor to: acquire the time-series input data; supply a plurality of input nodes of the model with a plurality of input values corresponding to input data at one time point in the time-series input data; store values of hidden nodes; compute a conditional probability of each input value at the one time point on a condition that an input data sequence has occurred, based on the input data sequence before the one time point in the time-series input data, on the stored values of hidden nodes, and on weight parameters between (i) a plurality of hidden nodes and a plurality of input values corresponding to input data at each time point before the one time point in the input data sequence and (ii) the plurality of input nodes and corresponding hidden nodes at the one time point; and perform a learning process that further increases a conditional probability of input data occurring at the one time point on the condition that the input data sequence has occurred, by adjusting the weight parameters. 2. The learning apparatus according to claim 1, wherein the instructions further cause the processor to: compute a conditional probability of each hidden node value at the one time point on the condition that the input data sequence has occurred, based on the input data sequence before the one time point in the time-series input data and on weight parameters between (i) a plurality of hidden nodes and a plurality of input values corresponding to input data at each time point before the one time point in the input data sequence and (ii) the plurality of input nodes and corresponding hidden nodes at the one time point. 3. The learning apparatus according to claim 2, wherein the instructions further cause the processor to: sample each hidden node value at the one time point using the conditional probability of each hidden node at the one time point. 4. The learning apparatus according to claim 3, wherein the instructions further cause the processor to: update a plurality of update parameters, which are based on the hidden nodes and input data sequence before the one time point in the time-series input data, from values at a previous time point to values at the one time point based on each update parameter value and on the corresponding hidden node values and input values in input data to be reflected next, wherein the computing of the conditional probability of each input value at the one time point and the computing of the conditional probability of each hidden node at the one time point include using the plurality of update parameters to compute the conditional probabilities of each hidden node and each input data value at the one time point on the condition that the input data sequence has occurred. 5. The learning apparatus according to claim 4, wherein the weight parameters are based on learning parameters that are learning targets and on predefined parameters that change in a predetermined manner according to a time point difference between the hidden nodes and input data at a time point before the one time point in the input data sequence and hidden nodes and input data at the one time point. 6. The learning apparatus according to claim 5, wherein the update parameters are based on: (i) input values corresponding to input data at each time point in the input data sequence and (ii) the predefined parameters in the weight parameters between the input values corresponding to the input data at each time point in the input data sequence and hidden nodes or input nodes that are targets, and (iii) the hidden nodes at each time point and (iv) the predefined parameters in the weight parameters between the hidden nodes at each time point and hidden nodes or input nodes that are targets. 7. The learning apparatus according to claim 6, wherein performing the learning process includes: computing update amounts of the learning parameters in the weight parameters for one hidden node at the one time point, based on the value of the one hidden node at the one time point and the conditional probability of the value of the one hidden node at the one time point on the condition that the input data sequence has occurred; and changing a ratio by which the update amounts are reflected in the learning parameters, according to the conditional probability of input data occurring at a following time point that is after the one time point. 8. The learning apparatus according to claim 7, wherein the changing of the ratio includes changing the ratio by which the update amounts are reflected in the learning parameters, according to the conditional probability of a hidden node value occurring at a predetermined plurality of following time points that are after the one time point. 9. The learning apparatus according to claim 8, wherein the changing of the ratio by which the update amounts are reflected in the learning parameters includes changing the ratio by which the update amounts are reflected in the learning parameters, according to at least one of a sum, a weighted sum, a product, and a weighted product of the conditional probability of the hidden node value occurring at the predetermined plurality of following time points that are after the one time point. 10. The learning apparatus according to claim 7, wherein the performing the learning process includes: storing the update amounts at the one time point in a FIFO sequence; and extracting from the FIFO sequence the update amounts at the following time point or a time point after the following time point, and the changing the ratio by which the update amounts are reflected in the learning parameters includes changing the ratio by which the update amounts extracted from the FIFO sequence are reflected in the learning parameters, according to the conditional probability of the input data occurring at the following time point. 11. The learning apparatus according to claim 1, wherein the performing the learning process includes adjusting the weight parameters at a plurality of time points all together, in response to input data of the plurality of time points being newly acquired. 12. A learning method for learning a model corresponding to time-series input data, comprising: acquiring the time-series input data; supplying a plurality of input nodes of the model with a plurality of input values corresponding to input data at one time point in the time-series input data; storing values of hidden nodes; computing a conditional probability of each input value at the one time point on a condition that an input data sequence has occurred, based on the input data sequence before the one time point in the time-series input data, on the stored values of hidden nodes, and on weight parameters between (i) a plurality of hidden nodes and a plurality of input values corresponding to input data at each time point before the one time point in the input data sequence and (ii) the plurality of input nodes and corresponding hidden nodes at the one time point; and performing a learning process that further increases a conditional probability of input data occurring at the one time point on the condition that the input data sequence has occurred, by adjusting the weight parameters. 13. The learning method according to claim 12, comprising: computing a conditional probability of each hidden node value at the one time point on the condition that the input data sequence has occurred, based on the input data sequence before the one time point in the time-series input data and on weight parameters between (i) a plurality of hidden nodes and a plurality of input values corresponding to input data at each time point before the one time point in the input data sequence and (ii) the plurality of input nodes and corresponding hidden nodes at the one time point. 14. The learning method according to claim 13, comprising: sampling each hidden node value at the one time point using the conditional probability of each hidden node at the one time point. 15. The learning method according to claim 14, comprising: updating a plurality of update parameters, which are based on the hidden nodes and input data sequence before the one time point in the time-series input data, from values at a previous time point to values at the one time point based on each update parameter value and on the corresponding hidden node values and input values in input data to be reflected next, wherein the computing of the conditional probability of each input value at the one time point and the computing of the conditional probability of each hidden node at the one time point include using the plurality of update parameters to compute the conditional probabilities of each hidden node and each input data value at the one time point on the condition that the input data sequence has occurred. 16. A computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to acquire time-series input data; supply a plurality of input nodes of a model with a plurality of input values corresponding to input data at one time point in the time-series input data; store values of hidden nodes; compute a conditional probability of each input value at the one time point on a condition that an input data sequence has occurred, based on the input data sequence before the one time point in the time-series input data, on the stored values of hidden nodes, and on weight parameters between (i) a plurality of hidden nodes and a plurality of input values corresponding to input data at each time point before the one time point in the input data sequence and (ii) the plurality of input nodes and corresponding hidden nodes at the one time point; and perform a learning process that further increases a conditional probability of input data occurring at the one time point on the condition that the input data sequence has occurred, by adjusting the weight parameters. 17. The computer program product according to claim 16, wherein the instructions further cause the computer to: compute a conditional probability of each hidden node value at the one time point on the condition that the input data sequence has occurred, based on the input data sequence before the one time point in the time-series input data and on weight parameters between (i) a plurality of hidden nodes and a plurality of input values corresponding to input data at each time point before the one time point in the input data sequence and (ii) the plurality of input nodes and corresponding hidden nodes at the one time point. 18. The computer program product according to claim 17, wherein the instructions further cause the computer to: sample each hidden node value at the one time point using the conditional probability of each hidden node at the one time point. 19. The computer program product according to claim 18, wherein the instructions further cause the computer to: update a plurality of update parameters, which are based on the hidden nodes and input data sequence before the one time point in the time-series input data, from values at a previous time point to values at the one time point based on each update parameter value and on the corresponding hidden node values and input values in input data to be reflected next, wherein the computing of the conditional probability of each input value at the one time point and the computing of the conditional probability of each hidden node at the one time point include using the plurality of update parameters to compute the conditional probabilities of each hidden node and each input data value at the one time point on the condition that the input data sequence has occurred.