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mmixture

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The answer MMIXTURE has 0 possible clue(s) in existing crosswords.

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The word MMIXTURE is NOT valid in any word game. (Sorry, you cannot play MMIXTURE in Scrabble, Words With Friends etc)

There are 8 letters in MMIXTURE ( E1I1M3R1T1U1X8 )

To search all scrabble anagrams of MMIXTURE, to go: MMIXTURE?

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Scrabble results that can be created with an extra letter added to MMIXTURE

IMMIXTURE

7 letters out of MMIXTURE

MIXTURE

6 letters out of MMIXTURE

IMMURE ITERUM REMIXT

5 letters out of MMIXTURE

MERIT MIMER MIREX MITER MITRE MIXER MUREX MUTER REMIT REMIX TIMER UTERI

4 letters out of MMIXTURE

EMIR EMIT ETUI EXIT ITEM MIME MIRE MITE MIXT MURE MUTE RIME RITE TERM TIER TIME TIRE TRIM TRUE

3 letters out of MMIXTURE

EMU IRE MEM MET MIM MIR MIX MUM MUT REI REM RET REX RIM RUE RUM RUT TIE TUI TUX UMM UTE

2 letters out of MMIXTURE

EM ER ET EX IT ME MI MM MU RE TI UM UT XI XU

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Mmixture might refer to
In statistics, a mixture model is a probabilistic model for representing the presence of subpopulations within an overall population, without requiring that an observed data set should identify the sub-population to which an individual observation belongs. Formally a mixture model corresponds to the mixture distribution that represents the probability distribution of observations in the overall population. However, while problems associated with "mixture distributions" relate to deriving the properties of the overall population from those of the sub-populations, "mixture models" are used to make statistical inferences about the properties of the sub-populations given only observations on the pooled population, without sub-population identity information.
* Some ways of implementing mixture models involve steps that attribute postulated sub-population-identities to individual observations (or weights towards such sub-populations), in which case these can be regarded as types of unsupervised learning or clustering procedures. However, not all inference procedures involve such steps.
* Mixture models should not be confused with models for compositional data, i.e., data whose components are constrained to sum to a constant value (1, 100%, etc.). However, compositional models can be thought of as mixture models, where members of the population are sampled at random. Conversely, mixture models can be thought of as compositional models, where the total size reading population has been normalized to 1.
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