pseudolikelihood
Definition
Pseudolikelihood refers to an approximation of the exact likelihood function used to estimate parameters in the Ising model, which addresses the computational intractability of the model's normalizing constant that grows exponentially with the number of variables. Two versions exist: the joint pseudolikelihood (JPL), which replaces the exact likelihood with a product of conditional distributions of each variable given all remaining variables, and the disjoint pseudolikelihood (DPL), which analyzes each variable separately using logistic regression and reconstructs the network in a piecemeal fashion. Both pseudolikelihood approaches reduce computational burden compared to exact likelihood approaches, making them feasible for larger networks. Maximum pseudolikelihood estimators have been compared to maximum likelihood estimators for parameter estimation in the Ising model.
Sources: Keetelaar et al. (2024)
Related Terms
Applications
Pseudolikelihood and Ising Model
The Ising model is used in network analysis and represents variables in a graphical model framework. Pseudolikelihood approximations are necessary because the exact likelihood of the Ising model becomes computationally infeasible as the number of variables increases due to the exponential growth of its normalizing constant.
Sources: Keetelaar et al. (2024)
Pseudolikelihood and Edge Selection
Pseudolikelihood methods are used for parameter estimation in the Ising model.
Sources: Keetelaar et al. (2024)
Pseudolikelihood and Sample Size
The performance of pseudolikelihood estimators in the Ising model has been evaluated across different conditions.
Sources: Keetelaar et al. (2024)
