Unbiased resampling(smcUtils)
Unbiased resampling()所属R语言包：smcUtils
Resampling functions
重采样功能
译者：生物统计家园网 机器人LoveR
描述----------Description----------
A set of resampling functions with unbiased number of replicates.
重采样功能不带偏见的一些重复的一组。
用法----------Usage----------
multinomial.resample(weights, num.samples = length(weights))
residual.resample( weights, num.samples = length(weights), resample.function=multinomial.resample)
stratified.resample( weights, num.samples = length(weights))
systematic.resample( weights, num.samples = length(weights))
branching.resample( weights, num.samples = length(weights))
参数----------Arguments----------
参数：weights
a vector of normalized weights
一个归一化的权重向量
参数：num.samples
the number of samples to return (for "branching.resample", "num.samples" is the expected number of samples as the actual number is random)
的样本数返回（branching.resample，num.samples是预期数目的样本的实际数目是随机的）
参数：resample.function
the resampling function to use on the remainder
使用其余的重采样功能
Details
详细信息----------Details----------
'multinomial.resample' samples component i with probability "weights[i]", repeats this sampling "num.samples" times, and returns indices for the sampled components.
“multinomial.resample的样品组分i的概率权重[I]，重复这采样的num.samples，”时间，并返回指数的采样组件。
'residual.resample' deterministically copies "floor(weights)" number of each component and then performs "resample.function" on the remainder.
residual.resample确定性副本的地板（重量）的各成分的数量，然后执行，其余的的resample.function。
"stratified.resample" draws "num.samples" uniform random variables on the ((i-1)/num.samples,i/num.samples) intervals of (0,1). It then uses the inverse.cdf.weights function to determine which components to sample.
stratified.resample绘制num.samples（第（i-1）/ num.samples的i / num.samples）（0,1）的间隔均匀的随机变量。然后使用inverse.cdf.weights的功能，以确定哪些组件样品。
"systematic.resample" draws 1 uniform random variable on (0,1/num.samples), builds a sequence of "num.samples" numbers by sequentially adding "1/num.samples", and then uses "inverse.cdf.weights" to determine which components to sample.
systematic.resample“吸引1均匀分布的随机变量（0,1 / num.samples），依次添加1 / num.samples”，建立了一个序列num.samples的数量，然后使用 inverse.cdf.weights“以确定哪些组件进行采样。
"branching.resample" deterministically copies "floor(weights)" number of components and then draws another component i with probability equal to the residual for that component. Note: the actual number of components after resampling is random.
“branching.resample确定性的副本的地板（重量）的元件数量，然后绘制另一个组分i的概率等于为该组件的残余。注：组件后重采样的实际数量是随机的。
值----------Value----------
Returns a vector of length "num.samples" with indices for sampled components.
返回一个向量的长度“num.samples的”指数的采样组件。
（作者）----------Author(s)----------
Jarad Niemi
参考文献----------References----------
Schemes for Particle Filtering. _Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis_
Carpenter, J., Clifford, P., Fearnhead, P. An improved particle filter for non-linear problems. _IEEE proceedings - Radar, Sonar and Navigation_ *146*, 2-7
参见----------See Also----------
resample,renormalize.weights, inverse.cdf.weights,rep2id
resample，renormalize.weights，inverse.cdf.weights，rep2id
实例----------Examples----------
ws = renormalize.weights(runif(10))
multinomial.resample(ws)
residual.resample(ws,resample.function=stratified.resample)
stratified.resample(ws,15)
systematic.resample(ws)
branching.resample(ws)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-9-30 10:19:31
