A method for performing computer aided flow cytometry, the method comprising: performing a flow cytometric experiment on a sample containing particles with a flow cytometer; obtaining, from the flow cytometer, measurements of a set of N cytometric events for the particles in the sample subjected to the flow cytometric experiment; modeling, by using computing means, p-dimensional data obtained from the measurements of the set of N cytometric events using a mixture of G p-dimensional parametric distributions, where G is a number of clusters in the p-dimensional data that contain at least G clusters; providing, by using computing means, an initial estimate of the parameters of said G p-dimensional parametric distributions; iteratively, by using computing means, estimating updated parameters for each of the G p-dimensional parametric distributions, wherein said estimating comprises: calculating, for each event, a posteriori probabilities that said event is a member of each of said parametric distributions, identifying a subset of said events that are within a neighborhood of at least one of said distributions based on a comparison of an event location to center points of respective distributions, wherein a number of events included in the identified subset of said events is less than a number of events in the set, and calculating, for each of the parameters for each of the G p-dimensional parametric distributions, an updated estimate based on the calculated a posteriori probabilities for each event in the identified subset of said events, wherein said estimating is iterated at least once, and wherein subsequent calculation of the a posteriori probabilities is based on the updated estimate for the parameters; and after said iterative estimating, identifying, by using computing means, a gate indicating a population of the particles from each of the G p-dimensional parametric distributions using the updated parameter estimates. performing a flow cytometric experiment on a sample containing particles with a flow cytometer; obtaining, from the flow cytometer, measurements of a set of N cytometric events for the particles in the sample subjected to the flow cytometric experiment; modeling, by using computing means, p-dimensional data obtained from the measurements of the set of N cytometric events using a mixture of G p-dimensional parametric distributions, where G is a number of clusters in the p-dimensional data that contain at least G clusters; providing, by using computing means, an initial estimate of the parameters of said G p-dimensional parametric distributions; iteratively, by using computing means, estimating updated parameters for each of the G p-dimensional parametric distributions, wherein said estimating comprises: calculating, for each event, a posteriori probabilities that said event is a member of each of said parametric distributions, identifying a subset of said events that are within a neighborhood of at least one of said distributions based on a comparison of an event location to center points of respective distributions, wherein a number of events included in the identified subset of said events is less than a number of events in the set, and calculating, for each of the parameters for each of the G p-dimensional parametric distributions, an updated estimate based on the calculated a posteriori probabilities for each event in the identified subset of said events, calculating, for each event, a posteriori probabilities that said event is a member of each of said parametric distributions, identifying a subset of said events that are within a neighborhood of at least one of said distributions based on a comparison of an event location to center points of respective distributions, wherein a number of events included in the identified subset of said events is less than a number of events in the set, and calculating, for each of the parameters for each of the G p-dimensional parametric distributions, an updated estimate based on the calculated a posteriori probabilities for each event in the identified subset of said events, wherein said estimating is iterated at least once, and wherein subsequent calculation of the a posteriori probabilities is based on the updated estimate for the parameters; and after said iterative estimating, identifying, by using computing means, a gate indicating a population of the particles from each of the G p-dimensional parametric distributions using the updated parameter estimates.