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This might seem to be restrictive, but is Carlo (MCMC) algorithms such as Gibbs Sampling. justifiable since our modeling procedure already in- Readers may refer to [27] for a review of MCMC al- troduces “noise” due to erroneous tracing and test- gorithms. ing. 4.1.3 Graph Embedding • The spreading probability, denoted as , is a con- stant that is independent of other parameters such Computing the infection probabilities of individuals di- as the values of the states, the number of days one rectly will be computationally cumbersome, and we can has been infected, etc. utilize graph embedding [5] techniques in order to find suspicious infected individuals. (...) Note that in the graph each node may this individual has a constant probability to get re- have up to | | edges, but in the embedded graph, each moved. We use 1,−2 to denote this probability. node only has coordinates. Since the number of edges might be much more than , performing computations • For an individual at state ( ) = , it has a with the embedded coordinates is much more efficient constant probability → to be tested to be state than directly working with the original graph. ( ) = . 5.
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Score: 740757 - https://www.itu.int/en/publica.../files/basic-html/page116.html
Data Source: un
The functionalities achieved by introducing FDMR are (1) simple probable mode derivation process, (2) use of the rank order of the intra prediction modes in the probable mode derivation process, and (3) use of more than one probable mode. (...) In that sense, it is implicit and only intermediate prediction mode probability is accounted. (c) Always uses only one probable mode (i.e. (...) Table 7 shows the result of the additional experiments in order to compare ‘two Probable Modes only for blocks with 33 modes’ and ‘two Probable Modes for all blocks.’
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Score: 740361.87 - https://www.itu.int/wftp3/av-a...010_07_B_Geneva/JCTVC-B063.doc
Data Source: un
Warmer than usually with a probability of 45-60% may be in western half of Belarus, western Ukraine and southern Moldova. (...) In the rest of Central Asia the temperature above normal is expected with a 45-60% probability.                                       Figure 1. (...) An excessive precipitation is predicted (with probability up to 50 %) in the central and eastern regions of Yakutia and in the Far East region.
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Score: 739457.03 - https://public.wmo.int/en/medi...outlook-forum-neacof-21-moscow
Data Source: un
(See Fig.1~4) 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% FootballBusForemanMobile ctxIdxInc2,flagOff ctxIdxInc1,flagOff ctxIdxInc0,flagOff ctxIdxInc2,flagOn ctxIdxInc1,flagOn ctxIdxInc0,flagOn Fig.1 Probability distribution at each context 0% 20% 40% 60% 80% 100% FootballBusForemanMobile ctxIdxInc0,flagOff ctxIdxInc0,flagOn Fig.2 Probability distribution when ctxIdxInc = 0 0% 20% 40% 60% 80% 100% FootballBusForemanMobile ctxIdxInc1,flagOff ctxIdxInc1,flagOn Fig.3 Probability distribution when ctxIdxInc = 1 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% FootballBusForemanMobile ctxIdxInc2,flagOff ctxIdxInc2,flagOn Fig.4 Probability distribution when ctxIdxInc = 2 Fig.1 gives the whole distribution of three contexts. (...) If the base macroblock is coded as an intra mode, the probability of the base_mode_flag to be set increases up to 90% (refer to Fig.6) and in the opposite case that the base macroblock is coded as inter mode, the probability of the base_mode_flag to be set is about 55% (refer to Fig.7). The following figures show that the probability of base_mode_flag depends on MB type of base macroblock. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% BusFootballForemanMobile flagOff,baseInter flagOff,baseIntra flagOn,baseInter flagOn,baseIntra Fig.5 Probability distribution according to MB type of base macroblock 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% BusFootballForemanMobile flagOff,baseIntra flagOn,baseIntra Fig.6 Probability distribution when MB type of base macroblock is intra 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% BusFootballForemanMobile flagOff,baseInter flagOn,baseInter Fig.7 Probability distribution when MB type of base macroblock is inter To clarify the effect of MB type of base macroblock on the base_mode_flag, we have analyzed the probability distribution depending on MB type of base macroblock and the current context model using Palma test condition. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% FootballBusForemanMobile ctxIdxInc2,flagOff,baseInter ctxIdxInc2,flagOff,baseIntra ctxIdxInc1,flagOff,baseInter ctxIdxInc1,flagOff,baseIntra ctxIdxInc0,flagOff,baseInter ctxIdxInc0,flagOff,baseIntra ctxIdxInc2,flagOn,baseInter ctxIdxInc2,flagOn,baseIntra ctxIdxInc1,flagOn,baseInter ctxIdxInc1,flagOn,baseIntra ctxIdxInc0,flagOn,baseInter ctxIdxInc0,flagOn,baseIntra Fig.8 Probability distribution according to MB type of base macroblock and context model 0% 20% 40% 60% 80% 100% FootballBusForemanMobile ctxIdxInc0,flagOff,baseIntra ctxIdxInc0,flagOn,baseIntra Fig.9 Probability distribution when base macroblock is intra and ctxIdxInc = 0 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% FootballBusForemanMobile ctxIdxInc0,flagOff,baseInter ctxIdxInc0,flagOn,baseInter Fig.10 Probability distribution when base macroblock is inter and ctxIdxInc = 0 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% FootballBusForemanMobile ctxIdxInc1,flagOff,baseIntra ctxIdxInc1,flagOn,baseIntra Fig.11 Probability distribution when base macroblock is intra and ctxIdxInc = 1 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% FootballBusForemanMobile ctxIdxInc1,flagOff,baseInter ctxIdxInc1,flagOn,baseInter Fig.12 Probability distribution when base macroblock is inter and ctxIdxInc = 1 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1234 ctxIdxInc2,flagOff,baseIntra ctxIdxInc2,flagOn,baseIntra Fig.13 Probability distribution when base macroblock is intra and ctxIdxInc = 2 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1234 ctxIdxInc2,flagOff,baseInter ctxIdxInc2,flagOn,baseInter Fig.14 Probability distribution when base macroblock is inter and ctxIdxInc = 2 As shown figures, if MB type of base macroblock is inter, the probability of base_mode_flag is very similar to that of JSVM 2.0.
Language:English
Score: 739420.36 - https://www.itu.int/wftp3/av-a...te/2005_07_Poznan/JVT-P104.doc
Data Source: un
NOTE 2 – (Assessment over decades of probability levels). For the assessment of prediction methods over decades of probability levels (e.g. from 0.001% to 10% of time) calculate the test variable Vi values for each percentage of time (preferred values are 0.001, 0.002, 0.003, 0.005, 0.01, 0.02, 0.03, 0.05, 0.1, 0.2, 0.3, 0.5, 1, 2, 3, 5, and 10), take into account a weighting function and calculate the mean standard deviation and r.m.s. value of all these Vi values over the required decades of probability levels. 4 Testing variable for comparing rain attenuation predictions 4.1 Principles of the methodology Attenuation predictions are generally made for a number of transmission paths at a fixed set of probability levels. Data for comparison of prediction methods are to be tabulated at fixed probability levels, e.g. 0.001%, 0.01% and 0.1% of the year. (...) NOTE 2 – (Assessment over decades of probability levels). For the assessment of prediction methods over decades of probability levels (e.g. from 0.001% to 0.1% of time) calculate the test variable Vi values for each percentage of time (preferred values are 0.001, 0.002, 0.003, 0.005, 0.01, 0.02, 0.03, 0.05, and 0.1), take into account a weighting function and calculate the mean standard deviation and r.m.s. value of all these Vi values over the required decades of probability levels.
Language:English
Score: 739126 - https://www.itu.int/dms_pub/it...0a/04/R0A040000070002MSWE.docx
Data Source: un
NOTE 2 – (Assessment over decades of probability levels). For the assessment of prediction methods over decades of probability levels (e.g. from 0.001% to 0.1% of time) calculate the test variable Vi values for each percentage of time (preferred values are 0.001, 0.002, 0.003, 0.005, 0.01, 0.02, 0.03, 0.05, and 0.1), take into account a weighting function and calculate the mean standard deviation and r.m.s. value of all these Vi values over the required decades of probability levels. (...) The mean and standard deviation of the test variable are then calculated to provide the statistics for prediction method comparison. 2.2 Procedure Step 1a : For prediction methods of the probability of occurrence P , calculate the test variable as the natural logarithm of the ratio of predicted probability Pp ( d > D|a > A ) to measured probability Pm ( d > D|a > A ), for each attenuation threshold A and for each fade duration D defined in Tables I8b and II3b, and for each radio link: (4) where: P , i : test variable calculated for the i -th radio link. (...) The mean and standard deviation of the test variable are then calculated to provide the statistics for prediction method comparison. 3.2 Procedure Step 1 : For each attenuation threshold A and for each fade slope value ζ defined in Table II8b, calculate the test variable from the predicted exceedance probability Pp (ζ |  A ) and the measured exceedance probability Pm (ζ |  A ) for each radio link, as: (6) where:  i  : test variable calculated for the i -th radio link.
Language:English
Score: 736870.85 - https://www.itu.int/dms_pub/it...0a/04/R0A040000070001MSWE.docx
Data Source: un
Kohler Population Studies Center University of Pennsylvania Overall Structure of the Survey on Aging in SSA 2 major components: • Household Interview • Individual Interview • Both will be linked based on HH ID and individual ID Overall Sampling Design  Nationally representative stratified random sample of households that include at least 1 household member age 60 years and older  Household sample surveys:  Key source for data on social phenomena  Are among the most flexible methods of data collections  In theory almost any population-based subject can be investigated through household surveys  Only probability samples following well-established sampling procedures are suitable for making inferences from the sample population to the larger population that it is designed to represent  Snow-ball or convenience samples are not suitable for this survey Overall Sampling Design cont’d  Probability sampling in the context of household surveys:  Refers to the means by which elements of the target population are selected for inclusion in the survey  In order to be cost-effective, most household surveys are not implemented as simple random samples  Sampling procedure usually includes stratification to ensure that the selected sample actually is spread over geographic sub-areas and population subgroups  This sampling design usually uses clusters of households in order to keep costs to manageable level General Principals of the Survey on Aging in SSA Target population: individuals age 60+ and older Household sample: Nationally representative clustered random sample of households that include household members age 60+ yrs. Selection of household members: All regular household members age 60+ in the sampled household and their spouses if these are age-eligible and co-resident General Principals of the Survey on Aging in SSA Use of an existing sampling frame: clustered random sample of households can only be obtained from existing sampling frame which is a complete list of statistical units covering the target population  Census frame, complete list of villages/communities or sampling list from other nationally representative surveys  Sampling frame: is a complete list of sampling units that entirely covers the target population  Conventional sampling frame: list of enumeration areas (EA) from a recently completed census  EA: geographic area which usually groups a number of households together for convenient counting purposes General Principals of the Survey on Aging in SSA Stratification: process in which the sample is designed into sub- groups or strata that are as homogeneous as possible;  Within each stratum the sample is designed and selected independently; Two-stage cluster sampling procedure:  Cluster: a group of adjacent households which serves as the primary sampling unit (PSU) General Principals of the Survey on Aging in SSA Full coverage of the target population: should be nationally representative and cover 100% of the target population; that is no subpopulations age 60+ are systematically excluded; Probability sampling: sample should be obtained as probabilistic sample based on existing sampling frame using established sampling procedures;  Only way to obtain unbiased estimation and to be able to evaluate the sampling errors  Excluded are purposive sampling, quota sampling, and other uncontrolled non-probability methods because they cannot provide evaluation of precision and confidence of survey findings General Principals of the Survey on Aging in SSA Full coverage of the target population: should be nationally representative and cover 100% of the target population; that is no subpopulations age 60+ are systematically excluded; Probability sampling: sample should be obtained as probabilistic sample based on existing sampling frame using established sampling procedures;  Only way to obtain unbiased estimation and to be able to evaluate the sampling errors  Excluded are purposive sampling, quota sampling, and other uncontrolled non-probability methods because they cannot provide evaluation of precision and confidence of survey findings Sample Size  Sample size must take into account competing needs so that costs and precisions are optimally balanced  Sample size must also address the needs of users who desire for sub-populations of sub-areas domains  Sample size is determined by the trade-offs between survey precision, data quality, organizational capacities and survey budget;  In the case of Malawi this is about 2,000 respondents (men and women) Conducting a household listing and pre-selection of households  Data quality is enhances if eligible households are preselected for participating in the study  In many SSA countries recent and reliable household listings in EAs that carefully enumerates older individuals is not available  Hence, we suggest to conduct a specific household listing in selected EAs that provides a well-grounded basis for selecting respondents  Interviewers than interview only pre-selected eligible households  STEPS:  Household listing operation conducted before the survey  Pre-selection of households from this list  Selected Households are interviewed Overall Sampling Design cont’d  Two stage sample design is well-established approach for implementing household surveys  1st stage: select a sample of EAs with probability proportional to size (PPS);  Within each stratum a sample of predetermined number of EAs is selected independently with probability proportional to size, where size is measured in terms of older individuals age 60+;  If size of pop age 60+ is not available, and variations in age structures are relatively modest, then total pop size can be used  All households in the EAs are listed  2nd stage: after complete listing in EAs, a fixed number of households with individuals age 60+ is selected by equal probability sampling in the EAs Interviewing all individuals age 60+ in the HH Advantages:  Maximize the number of respondents for a given sample of HH  Cost effective to achieve the sample size  Analytical advantages so that interactions among spouses, within and between household variation of outcomes can be investigated Disadvantages:  Lower statistical power given the within household correlation of observations  Logistical challenges in the fieldwork Sample Take per Cluster  How many eligible individuals to interview per EA  DHS recommends 25-30 individuals  Because there will be more than 1 age-eligible individual per household, less than 24-30 households per PA need to be selected  If a sampled HH has 1.5 age-eligible individuals on average, than a sample take per cluster of 25-30 individuals results in the selection of 17-20 households per cluster  With 2,000 individuals sample size: 67-80 clusters have to be selected  If sample is stratified, these considerations should be conducted stratum- specific Sample Take per Cluster  This fixed sample take per cluster is:  Easy for survey management and implementation  But requires sampling weights that vary within clusters
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Score: 735180.96 - https://www.un.org/en/developm...15/ikohler-sampling-design.pdf
Data Source: un
Therefore, , the following section. for each vehicle, shortening the signal distance will result in better link success probability. However, the slope will be vanished, which means that when 5. (...) Thus, we small while the interference distance is big. propose to assume the coefficients α , β and γ are Similarly, we can find that has a modest 1 dependent on the Number of Surrounding receiving probability while has the lowest 3 interfering Vehicles (NSVs). The value of α , β and receiving probability. In order to optimize the PRR, γ will be found via regress in Section 6 for different the 0,1 , 0,2 and 0,3 need to be adjusted by moving the transmitter.
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Score: 731824.2 - https://www.itu.int/en/publica...2/files/basic-html/page52.html
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عربي 中文 English Français Русский Español Home Health Topics All topics » A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Resources » Fact sheets Facts in pictures Multimedia Publications Questions & answers Tools and toolkits Popular » Coronavirus disease (COVID-19) Ebola virus disease Air pollution Hepatitis Top 10 causes of death World Health Assembly » Countries All countries » A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Regions » Africa Americas South-East Asia Europe Eastern Mediterranean Western Pacific WHO in countries » Statistics Cooperation strategies Democratic Republic of the Congo »   Newsroom All news » News releases Statements Campaigns Commentaries Events Feature stories Speeches Spotlights Newsletters Photo library Media distribution list Headlines » Timeline: WHO's COVID-19 response »   Emergencies Focus on » COVID-19 pandemic Ebola virus disease outbreak DRC 2021 Syria crisis Crisis in Northern Ethiopia Afghanistan Crisis Latest » Disease Outbreak News Travel advice Situation reports Weekly Epidemiological Record WHO in emergencies » Surveillance Research Funding Partners Operations Independent Oversight and Advisory Committee Coronavirus disease outbreak (COVID-19) » WHO © Credits Data Data at WHO » Global Health Estimates Health SDGs Mortality Triple billion targets Data collections Dashboards » COVID-19 Dashboard Triple Billion Dashboard Health Equity monitor Mortality Highlights » GHO SCORE Insights and visualizations Data collection tools Reports World Health Statistics 2021 » WHO © Credits About WHO About WHO » People Teams Structure Partnerships Collaborating Centres Networks, committees and advisory groups Transformation Contact us » Governance » World Health Assembly Executive Board Election of Director-General Governing Bodies website Our Work » General Programme of Work WHO Academy Activities Initiatives Better health for everyone » WHO © Credits Skip to main content Access Home Alt+0 Navigation Alt+1 Content Alt+2 Emergencies preparedness, response Menu Home Alert and response operations Diseases Biorisk reduction Disease outbreak news Ebola virus disease update - west Africa Disease outbreak news 28 August 2014 Epidemiology and surveillance The total number of probable and confirmed cases in the current outbreak of Ebola virus disease (EVD) in the four affected countries as reported by the respective Ministries of Health of Guinea, Liberia, Nigeria, and Sierra Leone is 3069, with 1552 deaths. (...) The distribution and classification of the cases are as follows: Guinea, 647 cases (482 confirmed, 141 probable, and 25 suspected), including 430 deaths; Liberia, 1378 cases (322 confirmed, 674 probable, and 382 suspected), including 694 deaths; Nigeria, 17 cases (13 confirmed, 1 probable, and 3 suspected), including 6 deaths; and Sierra Leone, 1026 cases (935 confirmed, 37 probable, and 54 suspected), including 422 deaths. Confirmed, probable, and suspect cases and deaths from Ebola virus disease in Guinea, Liberia, Nigeria, and Sierra Leone Confirmed Probable Suspect Totals Guinea Cases 482 141 25 648 Deaths 287 141 2 430 Liberia Cases 322 674 382 1 378 Deaths 225 301 168 694 Nigeria Cases 13 1 3 17 Deaths 5 1 0 6 Sierra Leone Cases 935 37 54 1 026 Deaths 380 34 8 422 Totals Cases 1 752 853 464 3 069 Deaths 897 477 178 1 552 Note: Cases are classified as confirmed (any suspected or probable cases with a positive laboratory result); probable (any suspected case evaluated by a clinician, or any deceased suspected case having an epidemiological link with a confirmed case where it has not been possible to collect specimens for laboratory confirmation); or suspected (any person, alive or dead, suffering or having suffered from sudden onset of high fever and having had contact with: a suspected, probable or confirmed Ebola case, or a dead or sick animal; or any person with sudden onset of high fever and at least three of the following symptoms: headache, vomiting, anorexia/loss of appetite, diarrhoea, lethargy, stomach pain, aching muscles or joints, difficulty swallowing, breathing difficulties, or hiccup; or any person with unexplained bleeding; or any sudden, unexplained death).
Language:English
Score: 730780.2 - https://www.who.int/csr/don/2014_08_28_ebola/en/
Data Source: un
The basic relationship between the proportion of chil- dren dead by age group of mother and the probability of dying in childhood can be illustrated by a very simple example. (...) Since in a life table the probability of dying by age 4, q(4), must be greater than the probability of dying by age 2, q(2), a given proportion of children dead for women aged 22 would indicate lower mortality risks in an early-fertility population than in a late-fertility popula- tion. (...) CORRESPONDENCE BETWEEN OBSERVED PROPORTIONS OF CHIL- DREN DEAD BY AGE GROUP OF MOTHER AND ESTIMATED PROBABILITIES OF DYING ESTIMATING TIME TRENDS OF MORTALITY The method originally developed by Brass assumed that mortality was constant, so that cohort and period probabilities of dying were identical.
Language:English
Score: 730333.64 - https://www.un.org/en/developm...s/estimate/childmort/chap3.pdf
Data Source: un