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  <titleInfo>
    <title>Statistical Methods for Materials Science</title>
    <subTitle>The Data Science of Microstructure Characterization</subTitle>
  </titleInfo>
  <name type="personal">
    <namePart>Jeffrey P. Simmons</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Lawrence F. Drummy</namePart>
    <role>
      <roleTerm authority="marcrelator" type="code">B01</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Charles A. Bouman</namePart>
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      <roleTerm authority="marcrelator" type="code">B01</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Marc De Graef</namePart>
    <role>
      <roleTerm authority="marcrelator" type="code">B01</roleTerm>
    </role>
  </name>
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  <originInfo>
    <publisher>CRC Press</publisher>
    <dateIssued>20210331</dateIssued>
    <edition>1</edition>
    <issuance/>
  </originInfo>
  <language>
    <languageTerm authority="iso639-2b" type="code">g  </languageTerm>
  </language>
  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
  </language>
  <physicalDescription>
    <extent>514 p</extent>
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  <abstract>Data analytics has become an integral part of materials science. This book provides the practical tools and fundamentals needed for researchers in materials science to understand how to analyze large datasets using statistical methods, especially inverse methods applied to microstructure characterization. It contains valuable guidance on essential topics such as denoising and data modeling. Additionally, the analysis and applications section addresses compressed sensing methods, stochastic models, extreme estimation, and approaches to pattern detection.</abstract>
  <identifier type="isbn">9780367780289</identifier>
  <identifier type="stock number">Taylor &amp; Francis</identifier>
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