ANALYSIS OF PROTEIN ACTIVITY DATA BY GAUSSIAN STOCHASTIC PROCESS MODELS

1999 ◽  
Vol 9 (1) ◽  
pp. 145-160 ◽  
Author(s):  
Nancy J. McMillan ◽  
Jerome Sacks ◽  
William J. Welch ◽  
Feng Gao
Keyword(s):  
Stochastic Process ◽  
Process Models ◽  
Protein Activity ◽  
Gaussian Stochastic Process ◽  
Activity Data

The effects of health histories on stochastic process models of aging and mortality

1995 ◽  
Vol 34 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Anatoli I. Yashin ◽  
Kenneth G. Manton ◽  
Max A. Woodbury ◽  
Eric Stallard
Keyword(s):  
Stochastic Process ◽  
Process Models

Replicating the Recovery following the 2014 South Napa Earthquake using Stochastic Process Models

2018 ◽  
Vol 34 (3) ◽  
pp. 1247-1266 ◽  
Author(s):  
Hua Kang ◽  
Henry V. Burton ◽  
Haoxiang Miao
Keyword(s):  
Stochastic Process ◽  
Repair Time ◽  
Process Models ◽  
Time Data ◽  
Data Set ◽  
Damage Data ◽  
Exogenous Factors ◽  
Recovery Models ◽  
Statistical Relationships ◽  
Recovery Trajectory

Post-earthquake recovery models can be used as decision support tools for pre-event planning. However, due to a lack of available data, there have been very few opportunities to validate and/or calibrate these models. This paper describes the use of building damage, permitting, and repair data from the 2014 South Napa Earthquake to evaluate a stochastic process post-earthquake recovery model. Damage data were obtained for 1,470 buildings, and permitting and repair time data were obtained for a subset (456) of those buildings. A “blind” prediction is shown to adequately capture the shape of the recovery trajectory despite overpredicting the overall pace of the recovery. Using the mean time to permit and repair time from the acquired data set significantly improves the accuracy of the recovery prediction. A generalized model is formulated by establishing statistical relationships between key time parameters and endogenous and exogenous factors that have been shown to influence the pace of recovery.

scholarly journals Robust Gaussian stochastic process emulation

2018 ◽  
Vol 46 (6A) ◽  
pp. 3038-3066 ◽  
Author(s):  
Mengyang Gu ◽  
Xiaojing Wang ◽  
James O. Berger
Keyword(s):  
Stochastic Process ◽  
Gaussian Stochastic Process

On the modeling of linear system input stochastic processes with given accuracy and reliability

2018 ◽  
Vol 24 (2) ◽  
pp. 129-137
Author(s):  
Iryna Rozora ◽  
Mariia Lyzhechko
Keyword(s):  
Banach Space ◽  
Stochastic Process ◽  
Linear System ◽  
Stochastic Processes ◽  
Impulse Response Function ◽  
Output Process ◽  
Gaussian Stochastic Process ◽  
System Input ◽  
Time Invariant ◽  
Gaussian Stochastic Processes

AbstractThe paper is devoted to the model construction for input stochastic processes of a time-invariant linear system with a real-valued square-integrable impulse response function. The processes are considered as Gaussian stochastic processes with discrete spectrum. The response on the system is supposed to be an output process. We obtain the conditions under which the constructed model approximates a Gaussian stochastic process with given accuracy and reliability in the Banach space{C([0,1])}, taking into account the response of the system. For this purpose, the methods and properties of square-Gaussian processes are used.

scholarly journals The maximum distribution of a Gaussian stochastic process indexed by a local field

1989 ◽  
Vol 46 (1) ◽  
pp. 69-87 ◽  
Author(s):  
Steven N. Evans
Keyword(s):  
Stochastic Process ◽  
Vector Space ◽  
Compact Subset ◽  
Local Field ◽  
Asymptotic Expression ◽  
Independent Interest ◽  
Gaussian Stochastic Process ◽  
High Level

AbstractWe consider continuous Gaussian stochastic process indexed by a compact subset of a vector space over a local field. Under suitable conditions we obtain an asymptotic expression for the probability that such a process will exceed a high level. An important component in the proof of these results is a theorem of independent interest concerning the amount of ‘time’ which the process spends at high levels.

Models of Movement: A Review of Alternative Methods

1980 ◽  
Vol 12 (12) ◽  
pp. 1383-1404 ◽  
Author(s):  
A Pickles
Keyword(s):  
Stochastic Process ◽  
Transition Rate ◽  
Process Models ◽  
Alternative Methods ◽  
Occupancy Models ◽  
Alternative Approaches ◽  
Fixed State

This paper reviews methods available to analyse movement and in particular migration. Stochastic process models seem able to provide a framework for microanalysis which can incorporate much of the complexity of such processes. However, a consideration of the effect of macro-constraints, in the form of limited opportunities for movement and of interhousehold competition, leads to a distinction between fixed transition rate and fixed state occupancy models. Alternative approaches to fixed state occupancy models are considered, and some of their potential strengths and weaknesses are discussed.

Sign in / Sign up

Export Citation Format

Share Document