# \#mle

**URL:** https://discourse.julialang.org/tag/mle/493.md

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## [Get MLE parameters (e.g., p-value, confidence intervals...) from a Turing model using Optim.jl](https://discourse.julialang.org/t/get-mle-parameters-e-g-p-value-confidence-intervals-from-a-turing-model-using-optim-jl/101433)

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**Author:** [@DominiqueMakowski](https://discourse.julialang.org/u/DominiqueMakowski)\
**Replies:** 3\
**Last updated:** [July 12, 2023, 9:11pm UTC](https://discourse.julialang.org/t/get-mle-parameters-e-g-p-value-confidence-intervals-from-a-turing-model-using-optim-jl/101433 "2023-07-12T21:11:03Z")

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EDIT: There is now PR to add this feature to Turing This might be a very silly question… but assuming the following simple linear model: using Turing using DataFrames using LinearAlgebra x = \[0, 1, 2, 3, 4, 5, 6, 7,…

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## [ModelingToolkit & Distributions for MLE estimation](https://discourse.julialang.org/t/modelingtoolkit-distributions-for-mle-estimation/97613)

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**Author:** [@dmetivie](https://discourse.julialang.org/u/dmetivie)\
**Replies:** 4\
**Last updated:** [April 19, 2023, 4:17pm UTC](https://discourse.julialang.org/t/modelingtoolkit-distributions-for-mle-estimation/97613 "2023-04-19T16:17:04Z")

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I am trying to use ModelingToolKit.jl to write a maximum loglikelihood estimator for generic distributions eg Univariate, Mixture, Multivariate etc. using ModelingToolkit, Optimization, OptimizationOptimJL using Distrib…

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## [Distributions fit\_mle of distribution type vs distributions with parameters](https://discourse.julialang.org/t/distributions-fit-mle-of-distribution-type-vs-distributions-with-parameters/75228)

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**Author:** [@dmetivie](https://discourse.julialang.org/u/dmetivie)\
**Replies:** 6\
**Last updated:** [April 4, 2023, 8:53am UTC](https://discourse.julialang.org/t/distributions-fit-mle-of-distribution-type-vs-distributions-with-parameters/75228 "2023-04-04T08:53:24Z")

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I try the following, but it does not work. using Distributions p = 0.2 x = rand(Bernoulli(0.2),50) fit\_mle(Bernoulli(p), x) MethodError: no method matching fit\_mle(::Bernoulli{Float64}, ::Vector{Bool}) I know that fi…

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## [Maximum likelihood optimization problem through Optim.jl](https://discourse.julialang.org/t/maximum-likelihood-optimization-problem-through-optim-jl/84096)

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**Author:** [@Jian\_ZUO](https://discourse.julialang.org/u/Jian_ZUO)\
**Replies:** 14\
**Last updated:** [July 12, 2022, 1:11pm UTC](https://discourse.julialang.org/t/maximum-likelihood-optimization-problem-through-optim-jl/84096 "2022-07-12T13:11:50Z")

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Hi, I am trying to solve a likelihood function in Optim as follows: I have some increments which are gamma-distributed (Ga(a\*t, β)): det\_x = \[0.0175, 0.0055, 0.0059\] # increments det\_t = \[185, 163, 167\] # correspondi…

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## [Optim.jl wrongly reporting success](https://discourse.julialang.org/t/optim-jl-wrongly-reporting-success/53174)

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**Author:** [@Albert\_Zevelev](https://discourse.julialang.org/u/Albert_Zevelev)\
**Replies:** 4\
**Last updated:** [January 13, 2021, 6:44pm UTC](https://discourse.julialang.org/t/optim-jl-wrongly-reporting-success/53174 "2021-01-13T18:44:17Z")

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I’m solving for the MLE. using Distributions, Random, Optim; DGP\_True = LogNormal(17,7); Random.seed!(123); const d\_train = rand(DGP\_True, 1\_000) const d\_test = rand(DGP\_True, 1\_000) # LogNormal function loglike(θ) …

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## [Recover MLE fit parameters in vector form](https://discourse.julialang.org/t/recover-mle-fit-parameters-in-vector-form/47972)

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**Author:** [@Albert\_Zevelev](https://discourse.julialang.org/u/Albert_Zevelev)\
**Replies:** 4\
**Last updated:** [October 8, 2020, 2:53pm UTC](https://discourse.julialang.org/t/recover-mle-fit-parameters-in-vector-form/47972 "2020-10-08T14:53:38Z")

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Suppose I fit some data using Distributions, Random; DGP\_True = LogNormal(0,1); Random.seed!(123); const d\_train = rand(DGP\_True, 1\_000); D1=Distributions.fit\_mle(LogNormal, d\_train) #Gives a fit distribution julia\> D1…

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## [Find number of parameters in a distribution](https://discourse.julialang.org/t/find-number-of-parameters-in-a-distribution/47961)

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**Author:** [@Albert\_Zevelev](https://discourse.julialang.org/u/Albert_Zevelev)\
**Replies:** 3\
**Last updated:** [October 8, 2020, 2:43am UTC](https://discourse.julialang.org/t/find-number-of-parameters-in-a-distribution/47961 "2020-10-08T02:43:11Z")

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Suppose I’m looping over a large vector of distributions from Distributions.jl. How can I automatically find how many parameters there are in a distribution? I want a function nparams(d) where: nparams(Normal)=2 since…