scholarly journals A Dynamic State-Space Model of Coded Political Texts

2013 ◽  
Vol 21 (2) ◽  
pp. 217-232 ◽  
Author(s):  
Martin Elff
Keyword(s):  
Monte Carlo ◽  
Empirical Bayes ◽  
Marginal Likelihood ◽  
State Space Model ◽  
Dynamic State ◽  
Model Parameters ◽  
Policy Space ◽  
Likelihood Principle ◽  
Size Adjustment ◽  
Sample Size Adjustment

This article presents a new method of reconstructing actors' political positions from coded political texts. It is based on a model that combines a dynamic perspective on actors' political positions with a probabilistic account of how these positions are translated into emphases of policy topics in political texts. In the article it is shown how model parameters can be estimated based on a maximum marginal likelihood principle and how political actors' positions can be reconstructed using empirical Bayes techniques. For this purpose, a Monte Carlo Expectation Maximization algorithm is used that employs independent sample techniques with automatic Monte Carlo sample size adjustment. An example application is given by estimating a model of an economic policy space and a noneconomic policy space based on the data from the Comparative Manifesto Project. Parties' positions in policy spaces reconstructed using these models are made publicly available for download.

scholarly journals Characterization of tumor heterogeneity by latent haplotypes: a sequential Monte Carlo approach

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4838 ◽  
Author(s):  
Oyetunji E. Ogundijo ◽  
Xiaodong Wang
Keyword(s):  
Monte Carlo ◽  
State Space ◽  
Tumor Heterogeneity ◽  
Sequential Monte Carlo ◽  
State Space Model ◽  
Sequential Algorithm ◽  
Model Parameters ◽  
Single Nucleotide Variants ◽  
Time Step ◽  
Allocation Model

Tumor samples obtained from a single cancer patient spatially or temporally often consist of varying cell populations, each harboring distinct mutations that uniquely characterize its genome. Thus, in any given samples of a tumor having more than two haplotypes, defined as a scaffold of single nucleotide variants (SNVs) on the same homologous genome, is evidence of heterogeneity because humans are diploid and we would therefore only observe up to two haplotypes if all cells in a tumor sample were genetically homogeneous. We characterize tumor heterogeneity by latent haplotypes and present state-space formulation of the feature allocation model for estimating the haplotypes and their proportions in the tumor samples. We develop an efficient sequential Monte Carlo (SMC) algorithm that estimates the states and the parameters of our proposed state-space model, which are equivalently the haplotypes and their proportions in the tumor samples. The sequential algorithm produces more accurate estimates of the model parameters when compared with existing methods. Also, because our algorithm processes the variant allele frequency (VAF) of a locus as the observation at a single time-step, VAF from newly sequenced candidate SNVs from next-generation sequencing (NGS) can be analyzed to improve existing estimates without re-analyzing the previous datasets, a feature that existing solutions do not possess.

scholarly journals Exploring maternal nutrition counseling provided by health professionals during antenatal care follow-up: a qualitative study in Addis Ababa, Ethiopia-2019

BMC Nutrition ◽  
2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Matyas Atnafu Alehegn ◽  
Tsegaye Kebede Fanta ◽  
Agumas Fentahun Ayalew
Keyword(s):  
Pregnant Women ◽  
Study Design ◽  
Health Professionals ◽  
Maternal Nutrition ◽  
Addis Ababa ◽  
Nutrition Counseling ◽  
Institutional Barriers ◽  
Size Adjustment ◽  
Sample Size Adjustment ◽  
Pregnant Mothers

Abstract Background Nutritional awareness and practice of women during pregnancy could be determining their nutritional status, which significantly affects the outcome of pregnancy. Therefore this study aims to explore the maternal nutrition counseling provided by health professionals for pregnant women, Barriers to maternal nutrition, and major interventions. Methods A descriptive study design with a qualitative method by using ground theory tradition, based on constructivist research approach and Charmaz’s (2000) study design has been conducted from September-01/2019 _November-16/2019 among pregnant women who got ANC service in Addis Ababa, Ethiopia. A purposive sampling technique was used. Practical observations and in-depth interviews were conducted. The sample size adjustment has been carried out according to the information saturation obtained, and finally, 81 practical observations, In-depth interview with two center managers, nine health professionals and eleven term pregnant women has been conducted. An observational checklist and Semi-structured, open-ended questionnaires were used. Data, the environment, and methodological triangulation were carried out. A conceptual framework has been established based on the data collected about the whole process of maternal nutrition counseling during pregnancy. ATLAS TI software was utilized for information analysis. The results Most participants responded that maternal nutrition counseling provided to pregnant mothers is not adequate and neglected by most stakeholders. From 81 practical observations, health professionals counseled to mothers were 10 what to feed, 4 what to limit to consume, and 5 were counseled about what to eat during pregnancy. Close to all the respondents agreed on the importance of providing nutrition counseled by the nutritionists. Most of the study participants emphasized a shortage of time as primary barriers. Institutional Barriers, Professional Barriers, Maternal Barriers, and Community Barriers were major barriers to nutrition counseling. Conclusions Generally, maternal nutrition counseling provided to pregnant mothers was not adequate and neglected by most stakeholders. Shortage of time due to client flow, Institutional Barriers, Professional Barriers, Maternal Barriers, and Community Barriers were major categories of maternal nutritional counseling barriers. Information update and timely preparation were recommended to health professionals.

scholarly journals Statistical Analysis of Tritium Breeding Ratio Deviations in the DEMO Due to Nuclear Data Uncertainties

2021 ◽  
Vol 11 (11) ◽  
pp. 5234
Author(s):  
Jin Hun Park ◽  
Pavel Pereslavtsev ◽  
Alexandre Konobeev ◽  
Christian Wegmann
Keyword(s):  
Monte Carlo ◽  
Fusion Reactor ◽  
Nuclear Data ◽  
Data File ◽  
Nuclear Model ◽  
Model Parameters ◽  
Tritium Breeding ◽  
Tritium Breeding Ratio ◽  
Nuclear Data Library ◽  
Breeding Ratio

For the stable and self-sufficient functioning of the DEMO fusion reactor, one of the most important parameters that must be demonstrated is the Tritium Breeding Ratio (TBR). The reliable assessment of the TBR with safety margins is a matter of fusion reactor viability. The uncertainty of the TBR in the neutronic simulations includes many different aspects such as the uncertainty due to the simplification of the geometry models used, the uncertainty of the reactor layout and the uncertainty introduced due to neutronic calculations. The last one can be reduced by applying high fidelity Monte Carlo simulations for TBR estimations. Nevertheless, these calculations have inherent statistical errors controlled by the number of neutron histories, straightforward for a quantity such as that of TBR underlying errors due to nuclear data uncertainties. In fact, every evaluated nuclear data file involved in the MCNP calculations can be replaced with the set of the random data files representing the particular deviation of the nuclear model parameters, each of them being correct and valid for applications. To account for the uncertainty of the nuclear model parameters introduced in the evaluated data file, a total Monte Carlo (TMC) method can be used to analyze the uncertainty of TBR owing to the nuclear data used for calculations. To this end, two 3D fully heterogeneous geometry models of the helium cooled pebble bed (HCPB) and water cooled lithium lead (WCLL) European DEMOs were utilized for the calculations of the TBR. The TMC calculations were performed, making use of the TENDL-2017 nuclear data library random files with high enough statistics providing a well-resolved Gaussian distribution of the TBR value. The assessment was done for the estimation of the TBR uncertainty due to the nuclear data for entire material compositions and for separate materials: structural, breeder and neutron multipliers. The overall TBR uncertainty for the nuclear data was estimated to be 3~4% for the HCPB and WCLL DEMOs, respectively.

scholarly journals Parameter stability and consistency in an alongshore-current model determined with Markov chain Monte Carlo

2008 ◽  
Vol 10 (2) ◽  
pp. 153-162 ◽  
Author(s):  
B. G. Ruessink
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Parameter Stability ◽  
Small Uncertainty ◽  
Best Fit ◽  
Stability And Consistency

When a numerical model is to be used as a practical tool, its parameters should preferably be stable and consistent, that is, possess a small uncertainty and be time-invariant. Using data and predictions of alongshore mean currents flowing on a beach as a case study, this paper illustrates how parameter stability and consistency can be assessed using Markov chain Monte Carlo. Within a single calibration run, Markov chain Monte Carlo estimates the parameter posterior probability density function, its mode being the best-fit parameter set. Parameter stability is investigated by stepwise adding new data to a calibration run, while consistency is examined by calibrating the model on different datasets of equal length. The results for the present case study indicate that various tidal cycles with strong (say, >0.5 m/s) currents are required to obtain stable parameter estimates, and that the best-fit model parameters and the underlying posterior distribution are strongly time-varying. This inconsistent parameter behavior may reflect unresolved variability of the processes represented by the parameters, or may represent compensational behavior for temporal violations in specific model assumptions.

Motion Model of Two-Wheel Differential Drive Mobile Robot under Variable Load

2014 ◽  
Vol 668-669 ◽  
pp. 352-356 ◽  
Author(s):  
Zhi Hu Ruan ◽  
Niu Wang ◽  
Bing Xin Ran
Keyword(s):  
Mobile Robot ◽  
State Space Model ◽  
Response Characteristic ◽  
Model Parameters ◽  
Variable Load ◽  
Motion Model ◽  
Actual System ◽  
Wheel Load ◽  
Differential Drive ◽  
Entire Vehicle

Based on kinematics characteristic of two-wheeled differential drive mobile robot (WMR) and response characteristic of fact motor drive system, this paper presents the analysis method of the equivalent rotation inertia, and the entire vehicle load is assigned to each wheel, and then the wheel load is converted into the corresponding equivalent rotation inertia of the motor shaft of each wheel, and motion model of WMR are obtained for combining with quasi-equivalent (QE) state space model of double-loop direct current motor systems under variable load and kinematics model of WMR under the load changes. By using speed response data of the actual system and combining with genetic algorithm to accurately identify the model parameters. Finally, through experiments results of the WMR motion model and the second order model respectively comparing with the actual system which demonstrates the effectiveness of the proposing method and model.

Probabilistic Assessment of Wind Farm Active Power Based on Monte-Carlo Simulation

2013 ◽  
Vol 291-294 ◽  
pp. 536-540 ◽  
Author(s):  
Xin Wei Wang ◽  
Jian Hua Zhang ◽  
Cheng Jiang ◽  
Lei Yu
Keyword(s):  
Monte Carlo ◽  
Power Output ◽  
Wind Farm ◽  
Active Power ◽  
Assessment Methods ◽  
Probabilistic Methods ◽  
Model Parameters ◽  
Probability Assessment ◽  
Reliability Models ◽  
Reliability Parameters

The conventional deterministic methods have been unable to accurately assess the active power output of the wind farm being the random and intermittent of wind power, and the probabilistic methods commonly used to solve this problem. In this paper the multi-state fault model is built considering run, outage and derating state of wind turbine, and then the reliability model of the wind farm is established considering the randomness of the wind speed, the wind farm wake effects and turbine failure. The active wind farm output probability assessment methods and processes based on the Monte Carlo method. The related programs are written in MATLAB, and the probability assessment for active power output of a wind farm in carried out, the effectiveness and adaptability of built reliability models and assessment methods are illustrated by analysis of the effects of reliability parameters and model parameters on assessment results.

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