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# Sampling with Metropolis Hasting-Hastings

In Metropolis Hasting-Hastings sampling, if every draw of my proposal distribution (Q) is independent from the previous draw, is the convergence to the stationary distribution still guaranteed? To be more precise, every draw is a fresh draw from the proposal (Q) and the acceptance rate is calculated as follows:

 a = min{1,(P(X')Q(X))/(P(X)Q(X'))}


Where X' is the new drawn sample, and X is the current sample, and P is the actual distribution. Also, can we still call this an MCMC sampling technique?

# Sampling with Metropolis Hasting

In Metropolis Hasting sampling, if every draw of my proposal distribution (Q) is independent from the previous draw, is the convergence to the stationary distribution still guaranteed? To be more precise, every draw is a fresh draw from the proposal (Q) and the acceptance rate is calculated as follows:

 a = min{1,(P(X')Q(X))/(P(X)Q(X'))}


Where X' is the new drawn sample, and X is the current sample, and P is the actual distribution. Also, can we still call this an MCMC sampling technique?

# Sampling with Metropolis-Hastings

In Metropolis-Hastings sampling, if every draw of my proposal distribution (Q) is independent from the previous draw, is the convergence to the stationary distribution still guaranteed? To be more precise, every draw is a fresh draw from the proposal (Q) and the acceptance rate is calculated as follows:

 a = min{1,(P(X')Q(X))/(P(X)Q(X'))}


Where X' is the new drawn sample, and X is the current sample, and P is the actual distribution. Also, can we still call this an MCMC sampling technique?

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In Metropolis Hasting sampling, if every draw of my proposal distribution (Q) is independent from the previous draw, is the convergence to the stationary distribution still guaranteed? To be more precise, every draw is a fresh draw from the proposal (Q) and the acceptance rate is calculated as follows:

 a = min{1,(P(T'X')Q(TX))/(P(TX)Q(T'X'))}


Where X' is the new drawn sample, and X is the current sample, and P is the actual distribution. Also, can we still call this an MCMC sampling technique?

In Metropolis Hasting sampling, if every draw of my proposal distribution (Q) is independent from the previous draw, is the convergence to the stationary distribution still guaranteed? To be more precise, every draw is a fresh draw from the proposal (Q) and the acceptance rate is calculated as follows:

 a = min{1,(P(T')Q(T))/(P(T)Q(T'))}


Also, can we still call this an MCMC sampling technique?

In Metropolis Hasting sampling, if every draw of my proposal distribution (Q) is independent from the previous draw, is the convergence to the stationary distribution still guaranteed? To be more precise, every draw is a fresh draw from the proposal (Q) and the acceptance rate is calculated as follows:

 a = min{1,(P(X')Q(X))/(P(X)Q(X'))}


Where X' is the new drawn sample, and X is the current sample, and P is the actual distribution. Also, can we still call this an MCMC sampling technique?

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# Sampling with Metropolis Hasting

In Metropolis Hasting sampling, if every draw of my proposal distribution (Q) is independent from the previous draw, is the convergence to the stationary distribution still guaranteed? To be more precise, every draw is a fresh draw from the proposal (Q) and the acceptance rate is calculated as follows:

 a = min{1,(P(T')Q(T))/(P(T)Q(T'))}


Also, can we still call this an MCMC sampling technique?