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# Mostly a TODO for myself, but if anyone else wants to tackle this, be my guest.
There seems to be a bug if providing a model with input data defining a branching stochastic structure. E.g. defini…
Tasqu updated
8 months ago
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The Ornstein-Uhlenbeck kernel defines a stationary gaussian process that describes a mean-reverting stochastic process. It seems more adapted to our modeling problem than a gaussian kernel that doesn'…
rlouf updated
2 years ago
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Some of the modules use stochastic processes in the estimation of models. For example the CalculateHouseholdDvmt module estimates a binary logit model of the probability that a household has any vehic…
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describe how values are drawn from bounds in OM object and used to simulate dynamics
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# 📚 Documentation/Examples
@vr308 I was trying to implement this paper https://arxiv.org/pdf/2202.12979v1.pdf using the example provided (Gaussian_Process_Latent_Variable_Models_with_Stochastic_Varia…
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Hi, thank you for sharing your code. Can you provide more examples of models? Because the performance of A2C is not very good, I am trying to use my own data (including newly added signal_features) an…
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I am trying to think of how you would stochastically simulate a model with a periodic term within pygom. The common_models module intialisis a determiistic SIS model with periodic transmision in the f…
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# GP
- [GP for big data: Hensmen (2013)][2]
- [Stochastic Variational Inference for Fully Bayesian Sparse Gaussian Process Regression Models: Yu (2017)][3]
# SVI
- [Variational Auto-encoder][4]…
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Can we incorporate environmental stochasticity in our models? How is the Gillespie processed changed? See Gibson & Bruck, 2000.
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Hey,
do you mean by 'built-in plotting' to simply call a method that can plot results obtained from a stochastic process (such as Geometric Brownian Motion)?
I have conducted extensive outlier …