U.S. flag

An official website of the United States government

Dot gov

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Https

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

PubAg

Main content area

You searched for: Subject term Monte Carlo method Remove constraint Subject term: Monte Carlo method Subject term Bayesian theory Remove constraint Subject term: Bayesian theory Subject term parameter uncertainty Remove constraint Subject term: parameter uncertainty
Start Over

Search

6  Search Results

1 - 6 of 6
Number of results to display per page

Search Results

1. Addressing and reducing parameter uncertainty in quantitative microbial risk assessment by incorporating external information via Bayesian hierarchical modeling

Author:
Wolfgang Seis; Pascale Rouault; Gertjan Medema
Source:
Water research 2020 v.185 pp. 116202
ISSN:
0043-1354
Subject:
Bayesian theory; Monte Carlo method; Norovirus; decision making; irrigation; microbiological risk assessment; parameter uncertainty; research; risk; wastewater; wastewater treatment; water
Abstract:
... Probabilistic quantitative microbial risk assessment (QMRA) studies define model inputs as random variables and use Monte-Carlo simulation to generate distributions of potential risk outcomes. If local information on important QMRA model inputs is missing, it is widely accepted to justify assumptions about these model inputs by using external literature information. A question, which remains unexp ...
DOI:
10.1016/j.watres.2020.116202

2. Review: Sources of Hydrological Model Uncertainties and Advances in Their Analysis

Author:
Edom Moges; Yonas Demissie; Laurel Larsen; Fuad Yassin
Source:
Water 2020 v.13 no.1 pp. -
ISSN:
2073-4441
Subject:
Bayesian theory; Monte Carlo method; hydrologic models; parameter uncertainty; uncertainty analysis; water
Abstract:
... Despite progresses in representing different processes, hydrological models remain uncertain. Their uncertainty stems from input and calibration data, model structure, and parameters. In characterizing these sources, their causes, interactions and different uncertainty analysis (UA) methods are reviewed. The commonly used UA methods are categorized into six broad classes: (i) Monte Carlo analysis, ...
DOI:
10.3390/w13010028

3. BayFish: Bayesian inference of transcription dynamics from population snapshots of single-molecule RNA FISH in single cells

Author:
Mariana Gómez-Schiavon; Liang-Fu Chen; Anne E. West; Nicolas E. Buchler
Source:
Genome biology 2017 v.18 no.1 pp. 164
ISSN:
1474-760X
Subject:
Bayesian theory; Monte Carlo method; fluorescence in situ hybridization; gene induction; genes; messenger RNA; models; neurons; parameter uncertainty
Abstract:
... Single-molecule RNA fluorescence in situ hybridization (smFISH) provides unparalleled resolution in the measurement of the abundance and localization of nascent and mature RNA transcripts in fixed, single cells. We developed a computational pipeline (BayFish) to infer the kinetic parameters of gene expression from smFISH data at multiple time points after gene induction. Given an underlying model ...
DOI:
10.1186/s13059-017-1297-9
PubMed:
28870226
PubMed Central:
PMC5582403

4. Using Bayesian inference to account for uncertainty in parameter estimates in modelled invasive flowering rush

Author:
Pece V. Gorsevski
Source:
Remote sensing letters 2013 v.4 no.3 pp. 279-287
ISSN:
2150-7058
Subject:
Bayesian theory; Butomus umbellatus; Markov chain; Monte Carlo method; case studies; conservation areas; decision making; invasive species; models; parameter uncertainty; regression analysis; remote sensing; Ohio
Abstract:
... Quantification of uncertainty that arises from a number of sources can enhance our understanding of biophysical processes of invasive species and the use of effective techniques, which results in more informed decision-making. To predict invasive flowering rush (FR) (Butomus umbellatus L.) within high spatial resolution imagery (<20 cm) from Applanix 439 Digital Sensor System (DSS) (Applanix Corpo ...
DOI:
10.1080/2150704X.2012.724539

5. Efficient hydrological model parameter optimization with Sequential Monte Carlo sampling

Author:
Erwin Jeremiah; Scott A. Sisson; Ashish Sharma; Lucy Marshall
Source:
Environmental modelling & software 2012 v.38 no. pp. 283-295
ISSN:
1364-8152
Subject:
Bayesian theory; Monte Carlo method; computer software; hydrologic data; hydrologic models; methodology; parameter uncertainty; physical models; watershed hydrology
Abstract:
... Bayesian inference provides an ideal platform for assessing parameter uncertainty for complex physical models, such as conceptual hydrological models. Sequential Monte Carlo (SMC) samplers are well suited for its implementation as they are effective in sampling from posterior distributions with the non-linear dependency structures and multiple modes often present in hydrological models. A challeng ...
DOI:
10.1016/j.envsoft.2012.07.001

6. Multimodel Bayesian analysis of data-worth applied to unsaturated fractured tuffs

Author:
Dan Lu; Ming Ye; Shlomo P. Neuman; Liang Xue
Source:
Advances in water resources 2012 v.35 no. pp. 69-82
ISSN:
0309-1708
Subject:
Bayesian theory; Monte Carlo method; model uncertainty; parameter uncertainty; permeability; risk; tuff; water resources; Arizona
Abstract:
... To manage water resource and environmental systems effectively requires suitable data. The worth of collecting such data depends on their potential benefit and cost, including the expected cost (risk) of failing to take an appropriate decision. Evaluating this risk calls for a probabilistic approach to data-worth assessment. Recently we [39] developed a multimodel approach to optimum value-of-info ...
DOI:
10.1016/j.advwatres.2011.10.007