Questions tagged [deterministic]

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Deterministic trend in VECM/VAR

I have a question regarding VECMs with deterministic terms. Consider the following VECM $$\Delta y_t= A(B'y_{t−1}+c_0)+c_1+B_1\Delta y_{t-1}+\dots+B_q\Delta y_{t-q}+\epsilon_t.$$ Note, that this ...
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1answer
24 views

Differences between realization of the random variable and deterministic variable?

The first question is that can we classify variable into random variable and deterministic variable? The second question is that The possible values taken by a random variable"X"(Uppercase) are termed ...
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27 views

Dealing with deterministic simulations via Bayesian analysis

Problem Set-Up Suppose I have a random variable $a \sim \mathcal{U}(\cdot)$, that is distributed uniformly, and some other random variable $X \sim \mathcal{N}(\cdot)$, that is distributed normally. ...
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1answer
84 views

Generate value of variables for given correlation coefficient

I would like to generate test data for script used for correlation analysis between quite long variables. Is it possible for a given length of vectors, to generate in relatively simple way ...
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35 views

Deterministic Assignment to Treatment

When estimating causal effects, you want to compare individuals as similar as possible. It is from this need that stems the exchangeability (/ignorability) or conditional exchangeability (/ ...
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2answers
115 views

How can we best explain causality for the uninitiated?

How can we best explain causality in layman's terms? There seem to be two main types of causality. One is probabilistic causation, the other is called determinism in philosophic circles or just ...
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1answer
141 views

Are Gaussian Mixture Models stochastic or deterministic?

Each time we generate a gmm model, we obtain slightly different clusters. Can we hence say gmm is stochastic? We obtain the same clusters if a random seed is set; does this mean given a random seed, ...
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1answer
491 views

What is the difference between Markov chain approximation and variational approximation?

I know they are two different approximation approaches to explicit models(which require approximation, that is transforming a non-optimization problem to an optimization problem to avoid the ...
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1answer
966 views

What are some examples of application of reinforcement learning to systems without stochastic dynamics?

We typically see examples of reinforcement learning problems modeled as a Markov Decision Processes wherein the state transition probabilities are specified. If the system of interest does not have ...
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13 views

Evaluate the probability of my experiment being deteministic AKA what tis the chance of something which has never happened happening? [duplicate]

Short version Imagine I run the exact same experiment $n$ independent times and get $n$ times the same result. Can you put a lower bound on the probability of getting the same result the $n+1$ time I ...
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457 views

Stochastic or Deterministic Trend: Supported by the Augmented Dickey-Fuller Test

Below are the sequential steps/question regarding my problem: I am attempting to specify a VAR model in order to analyze impulse response functions. In plotting my first variable (Figure 1) I ...
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1answer
141 views

Monte carlo simulation to forecast growth of a loan portfolio

I have to forecast the future gold loan portfolio growth of a financial firm. I have past 36 month growth data. I am planning to use Monte Carlo simulation to forecast, but growth is a deterministic ...
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2answers
125 views

Deterministic classifier and input features

I call the function estimation based classifiers as deterministic, the ones which estimates the $f(x) = a'x+b$ directly, rather than estimating the conditional or joint probabilities directly. For ...
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141 views

How to describe deterministic optimisation algorithms using statistics?

I am solving a large set of nonlinear optimisation problems using different algorithms. I have compared their performance using performance profiles (see Dolan and Moré, 2002). These profiles are ...
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10k views

What is the difference between deterministic and stochastic model?

Simple Linear Model: $x=\alpha t + \epsilon_t$ where $\epsilon_t$ ~iid $N(0,\sigma^2)$ with $E(x) = \alpha t$ and $Var(x)=\sigma^2$ AR(1): $X_t =\alpha X_{t-1} + \epsilon_t$ where $\epsilon_t$ ~...
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426 views

Specifying deterministic terms in VECM in case of logarithmic varriables

I have constructed a VEC model to study real housing price dynamics in relation to demographic demand, real GDP and costs of mortgages. However, I am stuck with the choice of deterministic terms. ...
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1answer
26k views

Explain what is meant by a deterministic and stochastic trend in relation to the following time series process? [closed]

Explain what is meant by a deterministic and stochastic trend in relation to the following time series process? $y_t = c + y_{t-1} + \varepsilon_t$ where $\varepsilon_t\sim iid(0, \sigma^2)$ this ...
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2k views

Can a random variable be a deterministic function of other random variables yet be independent of them?

I was confused by what it means when a Random variable is a deterministic function of another Random variable yet is independent of it? How is this possible? Here's the question: Consider three ...
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1answer
128 views

Simulator - a deterministic function of random variables

In this paper, the first discussion (Universal latent variable representation by C. Andrieu, A. Doucet and A. Lee) authors state that Sampling exactly $Y \sim f(y|\theta)$ on a computer most often ...
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2answers
4k views

Are linear classifiers (SVM, Logistic Regression) deterministic?

I am just starting to learn about classification and have been playing around with some linear classifiers. I was wondering if linear classifiers are deterministic--given the same model parameters and ...
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25 views

Existence of data when mean vector and covariance matrix (p.s.d.) is fixed? [duplicate]

I would like to prove that for any positive semidefinite matrix $\Sigma_{n\times n}$ and any vector $\mu_{n\times1}$, are there always data points (no matter how many, as long as finite) whose ...
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1answer
919 views

Deterministic sampling from discrete distribution

I'm working on a generalization of the Min-Hash algorithm to allow the meaningful comparison of ordered values such as integers. The core trick is to use deterministic randomness as a replacement for ...
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2answers
3k views

Variance and covariance in the context of deterministic variables

Questions: Can we talk about: variance of a deterministic variable?; covariance between a deterministic variable and a stochastic variable?; covariance between two deterministic variables? Are these ...
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3k views

What is the difference between a stochastic and a deterministic trend?

Models with stochastic trends i.e., structural time series models are useful in some instances. Firstly, it may be hard to identify multiple structural breaks in the deterministic trend when the ...