Tootfinder

L’amigo del prete perde la relijon, l’amigo del dotore perde la salut, l’amigo del’avocato perde la causa.
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I avocati vive da carne rabia' i dotori da carne mala' i preti da carne morta
#veneto

La mala costumbre que tienen algunas personas de poner a todos en "para" cuando envían un correo electrónico. Al final no se sabe a quién va realmente dirigido. 🤷🏻‍♂️
Gracias por permitirme este instante de desahogo, querido fediverso.
#nx

Metropolis-adjusted Subdifferential Langevin Algorithm
Ning Ning
https://arxiv.org/abs/2507.06950 https://arxiv.org/pdf/2507.06950

Metropolis-adjusted Subdifferential Langevin Algorithm
The Metropolis-Adjusted Langevin Algorithm (MALA) is a widely used Markov Chain Monte Carlo (MCMC) method for sampling from high-dimensional distributions. However, MALA relies on differentiability assumptions that restrict its applicability. In this paper, we introduce the Metropolis-Adjusted Subdifferential Langevin Algorithm (MASLA), a generalization of MALA that extends its applicability to distributions whose log-densities are locally Lipschitz, generally non-differentiable, and non-convex…

Gaussian Invariant Markov Chain Monte Carlo
Michalis K. Titsias, Angelos Alexopoulos, Siran Liu, Petros Dellaportas
https://arxiv.org/abs/2506.21511 https:…

Gaussian Invariant Markov Chain Monte Carlo
We develop sampling methods, which consist of Gaussian invariant versions of random walk Metropolis (RWM), Metropolis adjusted Langevin algorithm (MALA) and second order Hessian or Manifold MALA. Unlike standard RWM and MALA we show that Gaussian invariant sampling can lead to ergodic estimators with improved statistical efficiency. This is due to a remarkable property of Gaussian invariance that allows us to obtain exact analytical solutions to the Poisson equation for Gaussian targets. These …

A ratos pareciera que los rivales de Tohš en primarias fueran los candidatos de derecha y no los de la izquierda progresista. Muy mala su campaña.
#Política #Chile #Toha

Efficient Mirror-type Kernels for the Metropolis-Hastings Algorithm
Nuo Guan, Xiyun Jiao
https://arxiv.org/abs/2506.06660 https://arx…

Efficient Mirror-type Kernels for the Metropolis-Hastings Algorithm
We propose a new Metropolis-Hastings (MH) kernel by introducing the Mirror move into the Metropolis adjusted Langevin algorithm (MALA). This new kernel uses the strength of one kernel to overcome the shortcoming of the other, and generates proposals that are distant from the current position, but still within the high-density region of the target distribution. The resulting algorithm can be much more efficient than both Mirror and MALA, while stays comparable in terms of computational cost. We …