# Haut PDF Using PMCMC in EM algorithm for stochastic mixed models: theoretical and practical issues

### Using PMCMC in EM algorithm for stochastic mixed models: theoretical and practical issues

**PMCMC**dans l’algorithme

**EM**pour des modeles mixtes stochastiques : enjeux théoriques et pratiques Sophie Donnet 1

**and**Adeline Samson 2 , 3 Abstract: Biological processes measured repeatedly among ...

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### EM algorithm coupled with particle filter for maximum likelihood parameter estimation of stochastic differential mixed-effects models

**and**designs (n, ...particles

**and**of the proposals involved the SMC ...results

**using**a biais

**and**RMSE criteria. More precisely,

**for**each condition (parameters, design, number ...

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### Online EM Algorithm for Hidden Markov Models

**using**Polyak-Ruppert averaging (Polyak, 1990, Ruppert, 1988).

**In**Figure 5, Polyak-Ruppert averaging is used starting from n avg = ...is,

**for**n > 8000, ˆ θ n is replaced by ...

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### Parametric inference for mixed models defined by stochastic differential equations

**theoretical**result to find this ...used

**in**the examples empirical ...by

**stochastic**approximation

**using**the Louis’ missing information principle ...[13]

**for**an averaged SAEM ...SAEM ...

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### Parameter Estimation in Nonlinear Mixed Effect Models Using saemix, an R Implementation of the SAEM Algorithm

**algorithm**has been shown to have good statistical properties

**in**various theo- retical ( Kuhn

**and**Lavielle ( 2005 ))

**and**

**practical**applications ( Girard

**and**Mentr´ e ( 2005 ...

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### Practical issues in medication compliance in hypertensive patients

**and**emotions,

**and**often rely on family relationships

**and**social ...involvement

**and**education of family members, counseling, home visits,

**and**nurse telephone ...Nurses, ...

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### saemix, an R version of the SAEM algorithm for parameter estimation in nonlinear mixed effect models

**in**ludes the on entration versus time data olle ted

**in**12 subje ts given a single oral dose of theophylline,

**and**

**for**whom 11 blood samples were olle ted over a period of 24 ...data ...

3

### CVaR hedging using quantization based stochastic approximation algorithm

**using**the ...Like

**in**the static framework, the mode of the CVaR hedged loss distribution has been translated near the mean

**and**

**in**order to reduce the right tail distribution, the CVaR ...

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### Discriminating thresholds as a tool to cope with imperfect knowledge in multiple criteria decision aiding: Theoretical results and practical issues

**in**the operational instruction

**and**

**in**the diﬀerent sources of ...is

**in**general the analyst that must decide about the most adequate way

**in**decision aiding to take into account ...

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### Properties of the Stochastic Approximation EM Algorithm with Mini-batch Sampling

**EM**)

**algorithm**(Demp- ster et ...Carlo

**EM**,

**Stochastic**Approximation

**EM**, Monte Carlo Markov Chain-SAEM

**and**others can be very long, since all data points are visited

**in**...

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### Anomaly Detection and Localisation using Mixed Graphical Models

**and**Φ = (φ iu ) i,u∈C×Q is a general ...(IGM)

**and**Gaussian Graphical Model (GGM).

**In**the Gaussian model, that is, when p takes the form of a Gaussian density, the partition function Z is easy ...

6

### Theoretical and numerical study of a few stochastic models of statistical physics

**in**the hydrodynamic limit Ginzburg–Landau type potential, that is H(x) = X ψ(x i ), where ψ is the single-site ...Sobolev

**in**- equality,

**and**she used an assumption of uniform convexity of the ...

267

### Inverse problems for linear parabolic equations using mixed formulations -Part 1 : Theoretical analysis

**practical**(i.e. numerical) viewpoint, as enhanced

**in**[21, 32]

**and**recently used

**in**[1, 2]

**for**inverse problems

**and**

**in**[27, 29]

**in**the close controllability ...

28

### Clustering and classification of fuzzy data using the fuzzy EM algorithm

**in**a probabilistic framework [49, 50, ...imprecise

**and**uncertain labels [52, 53, 54, ...classes

**in**a impre- cise way.

**For**example, [56] addresses the problem of image segmentation ...

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### A Practical Outlier Detection Approach for Mixed-Attribute Data

**in**selecting the number of components

**in**the beta ...used

**in**Dean

**and**Nugent (2013) to select the number of beta mixture ...use

**in**our method ICL-BIC to identify the optimal number ...

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### Theoretical and Practical Limits of Superdirective Antenna Arrays

**and**the small eﬃciencies

**for**very closely-spaced arrays

**and**the need

**for**negative resistances

**for**transforming the 125 arrays to parasitic ...ones.

**In**general, ...

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### A minimax and asymptotically optimal algorithm for stochastic bandits

**algorithm**satisfies another worthwhile property of finite-time instance near-optimality , see Section 2 of Lattimore ( 2015 )

**for**a detailed ...Contributions.

**In**this work, we put forward the ...

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### Stochastic Models for Solar Power

**In**this work we develop a

**stochastic**model

**for**the solar power at the surface of the ...a

**stochastic**model

**for**the so-called clear sky index to obtain a

**stochastic**model ...

16

### Practical cases and issues related to model fitting

**in**put data? What is the form of the relationship with each of the variable ? What are the relationships between the parameters of the local

**models**

**and**the strata characteristics ? ...

42

### A Bayesian MAP-EM Algorithm for PET Image Reconstruction Using Wavelet Transform

**and**time-frequency analysis tools have been widely used

**in**signal processing, but their application

**in**tomographic reconstruction is recent [8]–[12],

**and**still growing ...data, ...

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