What is mixture density estimation?
Mixture modelling is also known as semi-parametric density estimation and it can be placed in between two extremes: parametric and non-parametric density estimation. Non-parametric estimates typically take a form of a mixture density with a mixture component for every data point in the data set.
How do you find the density of a gas mixture?
Density is defined as mass per unit volume of a substance under specific conditions of temperature and pressure. The density of the gas is equal to its mass divided by the volume. You can calculate the molar mass of the substance once the density of the gas is known.
How to estimate the parameters of a mixture?
The method estimates parameters of the mixture by maximizing the log likelihood function (LLF) using an intelligent optimization algorithm (IOA), genetic algorithm (GA). To verify the performance of the proposed method, one of the already existing methods is applied in the simulation study and the practical case study.
Why are beta distributions bad for maximum likelihood parameter estimation?
Mixtures of beta distributions are a flexible tool for modeling data with values on the unit interval, such as methylation levels. However, maximum likelihood parameter estimation with beta distributions suffers from problems because of singularities in the log-likelihood function if some observations take the values 0 or 1.
Which is the best method for beta mixture estimation?
The hybrid algorithm between likelihood-based component un-mixing and moment-based parameter estimation is a robust and efficient method for beta mixture estimation. We provide an implementation of the method (“betamix”) as open source software under the MIT license.
Which is the best method for parameter estimation?
A parameter estimation method based on the idea of optimization is proposed. The method estimates parameters of the mixture by maximizing the log likelihood function (LLF) using an intelligent optimization algorithm (IOA), genetic algorithm (GA).