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M\[EAcute]todo da m\[AAcute]xima verossimilhan\[CCedilla]a - 18/11/2014, Vito \
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Exemplo: espectro de energia em que o n\[UAcute]mero de contagens y(x) = a + \
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Este \[EAcute] o conjunto de dados, tirado de um experimento e relocado por \
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dessa tabela, verifica-se que solucoes[[7]] \[EAcute] a raiz - vamos guardar \
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agora, as barras de erro podem ser calculadas com uma aproxima\[CCedilla]\
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Sempre graficar os residuos normalizados em fun\[CCedilla]ao das \
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vari\[AHat]ncia a partir da matriz das derivadas da fun\[CCedilla]\[ATilde]o \
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o m\[EAcute]todo anal\[IAcute]tico \[EAcute] mais indicado para repetir \
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\[EAcute] mais pr\[AAcute]tico criar uma fun\[CCedilla]\[ATilde]o que gera a \
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Cell["\<\
come\[CCedilla]ar com 100 replicas e ir aumentando aos poucos, se h\[AAcute] \
tempo!\
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Cell["\<\
n\[ATilde]o d\[AAcute] para ter certeza que \[EAcute] tendencioso, valores \
conferem em menos que 2 desvios padroes... tentar 1000 repeticoes\
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Cell["\<\
Conclus\[ATilde]o: parece que os par\[AHat]metros est\[ATilde]o ligeiramente \
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corrigir.\
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Cell["\<\
Esse resultado \[EAcute] muito ruim, mas ele vem da subestima\[CCedilla]\
\[ATilde]o da vari\[AHat]ncia por conta da assimetria da f.p. de Poisson. Por \
isso, fazemos outro ciclo...\
\>", "Text",
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Cell["mudou muito - iterar outra vez, com novos pesos", "Text",
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\[EHat]ncia...", "Text",
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Cell["\<\
compare com resultado da m\[AAcute]xima verossimilhan\[CCedilla]a: a = \
4,6(17)  e  b = -0,12(9)\
\>", "Text",
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Cell["\<\
Assim, NESTE caso em que a fun\[CCedilla]\[ATilde]o \[EAcute] linear nos par\
\[AHat]metros, o mmq se sai muito bem, DESDE QUE se refa\[CCedilla]a os c\
\[AAcute]lculos com pesos deduzidos dos valores ajustados, em um processo \
iterativo, at\[EAcute] a converg\[EHat]ncia. Usar Sqrt[ n experimental ] para \
o desvio-padr\[ATilde]o de uma grandeza com f.p. Poisson \[EAcute] uma \
aproxima\[CCedilla]\[ATilde]o inadequada quando n < 10 e muito ruim quando n \
~ 1.\
\>", "Text",
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MMQ para fun\[CCedilla]\[OTilde]es n\[ATilde]o-lineares em apenas 1 ou 2 par\
\[AHat]metros e lineares nos demais\
\>", "Title",
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Note que a principal mudan\[CCedilla]a por usar os pesos corretos \
est\[AAcute] no desvio-padr\[ATilde]o de \[Omega]\
\>", "Text",
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Criar uma fun\[CCedilla]\[ATilde]o que ajusta e devolve s\[OAcute] os par\
\[AHat]metros ajustados\
\>", "Text",
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Cell["\<\
Aten\[CCedilla]\[ATilde]o, n\[ATilde]o confundir o par\[AHat]metro da fun\
\[CCedilla]\[ATilde]o simulaFit - \[Omega]0 - com a vari\[AAcute]vel \[Omega] \
da fun\[CCedilla]\[ATilde]o \[Alpha] Sin[\[Omega] t] que entra em \
NonlinearModelFit\
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Cell["\<\
\[AGrave]s vezes, o algoritmo de NonlinearModelFit pode n\[ATilde]o encontrar \
o m\[IAcute]nimo com a precis\[ATilde]o estabelecida (quando n\[ATilde]o se \
estabelece um AccuracyGoal, a rotina usa AccuracyGoal=1/2 PrecisionGoal que, \
por default, \[EAcute] MachinePrecision=16 d\[IAcute]gitos. Assim, ele est\
\[AAcute] tentando encontrar os valores com 8 d\[IAcute]gitos de precis\
\[ATilde]o, que pode ser muito. Nesse caso, pode-se mudar simulaFit para \
incluir na chamada de NolinearModelFit a op\[CCedilla]\[ATilde]o AccuracyGoal\
\[Rule]5, por exemplo.\
\>", "Text",
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Cell["\<\
Neste caso, a tendenciosidade \[EAcute] pequena, muito menor que o \
desvio-padr\[ATilde]o do resultado, e n\[ATilde]o precisa ser corrigida - h\
\[AAcute] algo errado com os n\[UAcute]meros do texto! J\[AAcute] o \
desvio-padr\[ATilde]o estimado merece ser modificado:\
\>", "Text",
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Cell["Conclus\[ATilde]o: A= = 1270(300), \[Omega]=0,0615(20) ", "Text",
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Cell["\<\
Frequentemente \[EAcute] melhor desenhar a curva de soma dos res\[IAcute]duos \
em fun\[CCedilla]a\[OTilde] do par\[AHat]metro n\[ATilde]o-linear\
\>", "Subsection",
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quando n\[ATilde]o se observa o fen\[OHat]meno em toda sua faixa de varia\
\[CCedilla]\[ATilde]o, \[EAcute] muito pior\
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mude a incerteza e a fra\[CCedilla]\[ATilde]o do per\[IAcute]odo e veja como \
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\[EAcute] muito comum graficar-se o res\[IAcute]duo normalizado pelos \
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