Computing probability intervals under independency constraints
نویسنده
چکیده
Many AI researchers argue that probability theory is only capable of dealing with uncertainty in situations where a fully specified joint probability distribution is available, and conclude that it is not suitable for application in AI systems. Probability intervals, however, constitute a means for expressing incompleteness of information. We present a method for computing probability interval! for probabilities of interest from a partial specification of a joint probability distribution. Our method improves on earlier approaches by all owing for independency relation ships between statistical variables to be exploited .
منابع مشابه
Exact maximum coverage probabilities of confidence intervals with increasing bounds for Poisson distribution mean
A Poisson distribution is well used as a standard model for analyzing count data. So the Poisson distribution parameter estimation is widely applied in practice. Providing accurate confidence intervals for the discrete distribution parameters is very difficult. So far, many asymptotic confidence intervals for the mean of Poisson distribution is provided. It is known that the coverag...
متن کاملAn Experimental Study of Field Dependency in Altered Extreme Environments
Failure to address extreme environments constraints at the human-computer interaction level may lead to the commission of critical and potentially fatal errors. This experimental study addresses gaps in our current theoretical understanding of the impact of±Gz accelerations and field dependency independency on task performance in human-computer interaction. It investigates the effects of ±Gz ac...
متن کاملAdaptive Confidence Intervals for Regression Functions Under Shape Constraints
Adaptive confidence intervals for regression functions are constructed under shape constraints of monotonicity and convexity. A natural benchmark is established for the minimum expected length of confidence intervals at a given function in terms of an analytic quantity, the local modulus of continuity. This bound depends not only on the function but also the assumed function class. These benchm...
متن کاملDetermination of Maximum Bayesian Entropy Probability Distribution
In this paper, we consider the determination methods of maximum entropy multivariate distributions with given prior under the constraints, that the marginal distributions or the marginals and covariance matrix are prescribed. Next, some numerical solutions are considered for the cases of unavailable closed form of solutions. Finally, these methods are illustrated via some numerical examples.
متن کاملShrinking Horizon Model Predictive Control with Signal Temporal Logic Constraints under Stochastic Disturbances
We present Shrinking Horizon Model Predictive Control (SHMPC) for discrete-time linear systems with Signal Temporal Logic (STL) specification constraints under stochastic disturbances. The control objective is to maximize an optimization function under the restriction that a given STL specification is satisfied with high probability against stochastic uncertainties. We formulate a general solut...
متن کامل