In Solomonoff Induction the smallest program outputting observations up to the present point in time will continue to output _future_ observations as optimal predictions.
These future observations may be thought of as missing data for which optimal imputations are the output. In ordinary statistical imputation, there is a table of potential observations, not all of which have been made in all attributes (columns) of all cases (rows). These are represented by a special symbol indicating no data (such as "n/a") in the corresponding cells. In the case of a pure time series imputation, such as Solomonoff induction, the entire future is present as something equivalent to "missing data".* In the case of supplanting statistics used by the social sciences with Solomonoff induction, rather than a linear tape going off into the future, one has a spatial dimension representing simultaneity. In this event model selection based on lossless compression must not disqualify a model (self-extracting archive of minimal length) for failing to reproduce the symbols representing missing data and, instead, producing imputations. *An aside is that an intelligent observer is inducing a model of not just any future old future observations but a particular observer's decision process about what it will next observe, which means, interestingly, an imputed value function for sequential decision theory. This means there is no way to escape from AIXI, even if one tries to strip off the sequential decision mechanism. ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T36c83eb0aa31fc55-Ma73be751a158f74717b7908b Delivery options: https://agi.topicbox.com/groups/agi/subscription
