Latent diffusion models for survival analysis
نویسندگان
چکیده
منابع مشابه
Latent diffusion models for survival analysis
We consider Bayesian hierarchical models for survival analysis, where the survival times are modeled through an underlying diffusion process, which determines the hazard rate. We show how these models can be efficiently treated by means of Markov chain Monte Carlo techniques.
متن کاملContinuous 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...
متن کاملEvaluation of Survival Analysis Models for Predicting Factors Infuencing the Time of Brucellosis Diagnosis
Background:Brucellosis or Malta fever is one of the most common zoonotic diseases in the world. In addition to causing human suffering and dire economic impact on animals, due to the high prevalence of Brucellosis in the western regions of Isfahan province, this study aimed to analyze effective factors in the time of Brucellosis diagnosis using parametric and semi-parametric mo...
متن کاملLatent Variable Models for Hippocampal Sequence Analysis
VIRTUAL TUNING CURVES we only train the HMMs on spikes from PBEs; to determine if the inferred states encode position data, we compute virtual tuning curves in two ways: (A) by decoding RUN data using the PBE-only HMM, and then using the true position data to estimate a map from states to position, and (B) by using the Bayesian decoder to estimate position during PBEs, and to learn a map from t...
متن کاملMixture models: latent profile and latent class analysis
Latent class analysis (LCA) and latent profile analysis (LPA) are techniques that aim to recover hidden groups from observed data. They are similar to clustering techniques but more flexible because they are based on an explicit model of the data, and allow you to account for the fact that the recovered groups are uncertain. LCA and LPA are useful when you want to reduce a large number of conti...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Bernoulli
سال: 2010
ISSN: 1350-7265
DOI: 10.3150/09-bej217