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.
Language:English
Score: 740757

https://www.itu.int/en/publica.../files/basichtml/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.’
Language:English
Score: 740361.87

https://www.itu.int/wftp3/ava...010_07_B_Geneva/JCTVCB063.doc
Data Source: un
Warmer than usually with a probability of 4560% 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 4560% 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.
Language:English
Score: 739457.03

https://public.wmo.int/en/medi...outlookforumneacof21moscow
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%
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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%
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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/ava...te/2005_07_Poznan/JVTP104.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 > Da > A ) to measured probability Pm ( d > Da > 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 populationbased subject can be investigated through household surveys
Only probability samples following wellestablished sampling procedures are suitable for making inferences from the sample population to the larger population that it is designed to represent Snowball 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 costeffective, 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 subareas 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 ageeligible and coresident
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;
Twostage 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 nonprobability 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 nonprobability 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 subpopulations of subareas domains
Sample size is determined by the tradeoffs 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 preselection 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 wellgrounded basis for selecting respondents
Interviewers than interview only preselected eligible households
STEPS:
Household listing operation conducted before the survey
Preselection of households from this list
Selected Households are interviewed
Overall Sampling Design cont’d
Two stage sample design is wellestablished 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 2530 individuals
Because there will be more than 1 ageeligible individual per household, less than 2430 households per PA need to be selected
If a sampled HH has 1.5 ageeligible individuals on average, than a sample take per cluster of 2530 individuals results in the selection of 1720 households per cluster
With 2,000 individuals sample size: 6780 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
Language:English
Score: 735180.96

https://www.un.org/en/developm...15/ikohlersamplingdesign.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.
Language:English
Score: 731824.2

https://www.itu.int/en/publica...2/files/basichtml/page52.html
Data Source: un
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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 earlyfertility population than in a latefertility 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