{"title":"顺序蒙特卡罗方法","authors":"A. Doucet","doi":"10.1002/0471667196.ESS5089","DOIUrl":null,"url":null,"abstract":"Algorithm 1 Bootstrap particle filter (for i = 1, . . . , N) 1. Initialization (t = 0): (a) Sample x i 0 ∼ p(x0). (b) Set initial weights: w i 0 = 1/N. 2. for t = 1 to T do (a) Resample: sample ancestor indices ai t ∼ C({w j t−1}j=1). (b) Propagate: sample x i t ∼ p(xt | x ai t t−1). x i 0:t = {x ai t 0:t−1, x i t}. (c) Weight: compute w̃ i t = p(yt | x i t) and normalize w i t = w̃ i t/ ∑N j=1 w̃ j t . The ancestor indices {ai t}i=1 allow us to keep track of exactly what happens in each resampling step. Note the bookkeeping added to the propagation step 2b. 2/22 Bookkeeping – ancestral path","PeriodicalId":336063,"journal":{"name":"Handbook of Graphical Models","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"113","resultStr":"{\"title\":\"Sequential Monte Carlo Methods\",\"authors\":\"A. Doucet\",\"doi\":\"10.1002/0471667196.ESS5089\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Algorithm 1 Bootstrap particle filter (for i = 1, . . . , N) 1. Initialization (t = 0): (a) Sample x i 0 ∼ p(x0). (b) Set initial weights: w i 0 = 1/N. 2. for t = 1 to T do (a) Resample: sample ancestor indices ai t ∼ C({w j t−1}j=1). (b) Propagate: sample x i t ∼ p(xt | x ai t t−1). x i 0:t = {x ai t 0:t−1, x i t}. (c) Weight: compute w̃ i t = p(yt | x i t) and normalize w i t = w̃ i t/ ∑N j=1 w̃ j t . The ancestor indices {ai t}i=1 allow us to keep track of exactly what happens in each resampling step. Note the bookkeeping added to the propagation step 2b. 2/22 Bookkeeping – ancestral path\",\"PeriodicalId\":336063,\"journal\":{\"name\":\"Handbook of Graphical Models\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-08-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"113\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Handbook of Graphical Models\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/0471667196.ESS5089\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Handbook of Graphical Models","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/0471667196.ESS5089","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 113
摘要
算法1 Bootstrap粒子滤波(对于i = 1，…)1.答案为b。初始化(t = 0):(a)样本x i 0 ~ p(x0)。(b)设置初始权值:wi 0 = 1/N。2. (a)重新采样:样本祖先索引ai t ~ C({w j t−1}j=1)。(b)传播:样本x i t ~ p(xt | x ai t - 1)。X I 0:t = {X I t 0:t−1,X I t}。(c)权值:计算w i t = p(yt | x i t)，归一化w i t = w i t/∑N j=1 w j t。祖先索引{ai t}i=1使我们能够准确地跟踪每个重采样步骤中发生的情况。注意在传播步骤2b中添加的簿记。2/22簿记-祖传的路径
Algorithm 1 Bootstrap particle filter (for i = 1, . . . , N) 1. Initialization (t = 0): (a) Sample x i 0 ∼ p(x0). (b) Set initial weights: w i 0 = 1/N. 2. for t = 1 to T do (a) Resample: sample ancestor indices ai t ∼ C({w j t−1}j=1). (b) Propagate: sample x i t ∼ p(xt | x ai t t−1). x i 0:t = {x ai t 0:t−1, x i t}. (c) Weight: compute w̃ i t = p(yt | x i t) and normalize w i t = w̃ i t/ ∑N j=1 w̃ j t . The ancestor indices {ai t}i=1 allow us to keep track of exactly what happens in each resampling step. Note the bookkeeping added to the propagation step 2b. 2/22 Bookkeeping – ancestral path
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