A MULTILEVEL DISCRETE-TIME SURVIVAL LATENT VARIABLE MODEL

Continuous Time Survival in Latent Variable Models

We describe a general multivariate, multilevel framework for continuous time survival analysis that includes joint modeling of survival time variables and continuous and categorical observed and latent variables. The proposed framework is implemented in the Mplus software package. The survival time variables are modeled with nonparametric or parametric proportional hazard distributions and incl...

Topographic Transformation as a Discrete Latent Variable

Invariance to topographic transformations such as translation and shearing in an image has been successfully incorporated into feedforward mechanisms, e.g., "convolutional neural networks", "tangent propagation". We describe a way to add transformation invariance to a generative density model by approximating the nonlinear transformation manifold by a discrete set of transformations. An EM algo...

Dynamics of a discrete-time plant-herbivore model

A Latent-Variable Lattice Model

Markov random field (MRF) learning is intractable, and the approximation algorithms are computationally expensive. Since only a small subset of MRF is used frequently in computer vision, we characterize this subset with three concepts: (1) Lattice, (2) Homogeneity, and (3) Inertia; and design a non-markov high-bias low-variance model as an alternative to this subclass of MRF. Our goal is robust...

Discrete-time Multilevel Hazard Analysis

Combining innovations in hazard modeling with those in multilevel modeling, we develop a method to estimate discrete-time multilevel hazard models. We derive the likelihood of and formulate assumptions for a discrete-time multilevel hazard model with time-varying covariates at two levels. We pay special attention to assumptions justifying the estimation method. Next, we demonstrate file constru...