A predictive estimator, trained on a data corpus, is used to generate a probability estimate based a sequence of data related to an entity. The predictive estimator computes an instantaneous surprise score which is a quantification of a short-term deviation of a datum from the probability estimate. To compute the instantaneous surprise score, the predictive estimator is initialized with default values of the predictive estimator. Then, for each of data input of the datum to the predictive estimator, the instantaneous surprise score is calculated, corresponding to the deviation of the data input from the probability estimate. This generates an estimate of the probability of observing the datum given past data on the entity and the predictive estimator. The predictive estimator is updated with the datum and the time step advanced.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: generating, by a predictive estimator, a probability estimate for data related to an entity, the probability estimate based on a sequence of data received by the predictive estimator, the predictive estimator being trained on a data corpus to provide the probability estimate; and computing, with the predictive estimator, at a time step, an instantaneous surprise score, the instantaneous surprise score being a quantification of a short-term deviation of a datum from the probability estimate, the computing comprising: initializing the predictive estimator with default values of the predictive estimator; calculating, for each data input of the datum to the predictive estimator, the instantaneous surprise score corresponding to a deviation of the data input from the probability estimate; generating an estimate of the probability of observing the datum based on past data on the entity and the predictive estimator trained on the data corpus; updating the predictive estimator with the datum; and advancing the time step in the predictive estimator. 2. The method of claim 1, wherein the instantaneous surprise score is transformed according to at least one transformation comprising: a normalization by a probability estimate not particular to the entity, or by a function of such a probability estimate, a negated logarithm function, a truncation which limits either low or high values, or both, to be no smaller, or no greater than a lower and upper limit, a filter of a sequence of surprise scores or transformation of such scores, a normalization by one or more filters of instantaneous surprise scores or transformations of such scores, and a calibration function. 3. A method comprising: generating, by a latent parameter estimator, one or more latent parameter estimates for data related to an entity, the latent parameter estimates based on a sequence of data received by the latent parameter estimator, the latent parameter estimator being trained on a data corpus to provide the latent parameter estimate; and computing, with the latent parameter estimator, at a time step, an instantaneous surprise score, the instantaneous surprise score being a quantification of a change of latent parameter estimates, the computing comprising: initializing the latent parameter estimator with default values of the latent parameter estimator; calculating, for each data input of the datum to the latent parameter estimator, an instantaneous surprise score corresponding to a function of one or more of the latent parameter estimates; updating the latent parameter estimator with the current datum; and advancing the time step in the latent parameter estimator. 4. The method of claim 3, wherein the latent parameter model is a topic model. 5. The method of claim 4, wherein the latent parameter estimates are estimated topic allocations using data observed up to a given time point. 6. The method of claim 3, wherein the instantaneous surprise score is a deviation measure on the latent parameter estimates, or functions thereof, before and after they have been updated with the current datum. 7. The method of claim 3, wherein the instantaneous surprise score is a deviation measure between latent parameter estimates, or functions thereof, at least one of which has a rapid decay time-scale, and least one of which has a slower decay time-scale. 8. A method comprising: generating a latent parameter estimate based on a data set and a latent parameter model, the latent parameter model being trained on a data corpus of time series to generate the latent parameter estimate; computing, at each time step, two current latent parameter estimates, one derived from data accumulated in a forward direction from the first data point to the current time step, and the other derived from data accumulated in a backward direction from the last data point to the current time step; calculating, at each time step, two representative vectors based on the forward computation and the backward computation; calculating, at each time step, a deviation quantity between the representative vectors, or functions thereof, at the current time step or across nearby time steps; and determining whether a change point exists based on the calculated deviations. 9. The method of claim 8, wherein the representative vectors are the vectors of latent parameter estimates. 10. The method of claim 8, wherein the representative vectors are the probability estimates derived from the model and the latent parameter estimates. 11. The method of claim 8, wherein the deviation function is selected from any one of: a cosine distance, Euclidean distance, Jensen-Shannon divergence, Jeffrey's distance, and Hellinger coefficient. 12. The method of claim 8, wherein the latent parameter model is a topic model. 13. The method of claim 8 further comprising: generating a Monte-Carlo sample of a plurality of time-series, wherein the time-series is computed using a resampling procedure that does not presuppose a change-point to be present in the time-series; computing the plurality of deviation quantities substituting the resampled time series for the original time series; and normalizing the time-series of deviation quantities on the observed data with the time-series of deviation quantities from resampled data to generate an improved time-series of deviation quantities. 14. The method of claim 13, wherein the normalization is performed by subtracting the mean of the time-series of deviation quantities from resampled data from the time-series of deviation quantities from observed data to generate an improved time-series of deviation quantities. 15. The method of claim 14 further comprising: locating the maximum value of the corrected time-series over the time-series; computing a z-score at the location of the maximum value by dividing the corrected time-series by the standard deviation of the resampled time-series at the same location; and determining the statistical significance of a hypothesized change-point with the z-score. 16. The method of claim 1, wherein the data corpus is at least partially composed of synthesized data. 17. A non-transitory computer program product storing instructions that, when executed by at least one programmable processor, cause the at least one programmable processor to perform operations comprising: generating, by a predictive estimator, a probability estimate for data related to an entity, the probability estimate based on a sequence of data received by the predictive estimator, the predictive estimator being trained on a data corpus to provide the probability estimate; and computing, with the predictive estimator, at a time step, an instantaneous surprise score, the instantaneous surprise score being a quantification of a short-term deviation of a datum from the probability estimate, the computing comprising: initializing the predictive estimator with default values of the predictive estimator; calculating, for each data input of the datum to the predictive estimator, the instantaneous surprise score corresponding to a deviation of the data input from the probability estimate; generating an estimate of the probability of observing the datum based on past data on the entity and the model trained on the data corpus; updating the predictive estimator with the datum; and advancing the time step in the predictive estimator. 18. A system comprising: at least one programmable processor; and a non-transitory machine-readable medium storing instructions that, when executed by the at least one processor, cause the at least one programmable processor to perform operations comprising: generating, by a predictive estimator, a probability estimate for data related to an entity, the probability estimate based on a sequence of data received by the predictive estimator, the predictive estimator being trained on a data corpus to provide the probability estimate; and computing, with the predictive estimator, at a time step, an instantaneous surprise score, the instantaneous surprise score being a quantification of a short-term deviation of a datum from the probability estimate, the computing comprising: initializing the predictive estimator with default values of the predictive estimator; calculating, for each data input of the datum to the predictive estimator, the instantaneous surprise score corresponding to a deviation of the data input from the probability estimate; generating an estimate of the probability of observing the datum based on past data on the entity and the model trained on the data corpus; updating the predictive estimator with the datum; and advancing the time step in the predictive estimator.