Accession Number:

ADA151216

Title:

Inference and State Estimation for Stochastic Point Processes.

Descriptive Note:

Interim scientific rept. 1 Jan-31 Dec 84,

Corporate Author:

JOHNS HOPKINS UNIV BALTIMORE MD DEPT OF MATHEMATICAL SCIENCES

Personal Author(s):

• Karr,A. F.

Report Date:

1985-01-23

Pagination or Media Count:

14.0

Abstract:

Stochastic point processes are models of points distributed randomly in some space these points may represent, for example, locations or even trajectories of tracked objects, times and amounts of precipitation events, or failure times and modes of a complex system. This research project is directed toward two principal problems arising in applications of point processes statistical inference for point processes whose probability law is unknown entirely or in part, and state estimation for partially observed point processes, i.e., minimum mean squared error reconstruction, realization-by-realization, of random variables that are not directly observable. These problems are examined in several not disjoint contexts stationary point processes, Cox processes, multiplicative intensity processes and Poisson processes. Another thrust of the research is inference for stochastic processes based on point process samples, with the particular goal to investigate inference and state estimation for random fields given point process samples. This report documents results in the research for this period. Additional keywords Markov processes. Author

Descriptors:

• *STOCHASTIC PROCESSES
• *STATISTICAL INFERENCE
• *ESTIMATES
• RANDOM VARIABLES
• PROBABILITY
• INTENSITY
• TRACKING
• STATIONARY
• SAMPLING
• TRAJECTORIES
• MARKOV PROCESSES
• MULTIPLICATION FACTOR
• POISSON DENSITY FUNCTIONS

Subject Categories:

• Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE