---
name: metropolis_hastings
kind: function
lang: cpp
domain: datascience
version: "1.0.0"
purity: pure
signature: "MHResult mh_run_1d(const std::function<double(double)>& log_pdf, double x0, double sigma, size_t n, double* out, Rng&); MHResult mh_run_nd(const std::function<double(const double*)>& log_pdf, const double* x0, const double* sigma, int d, size_t n, double* out, Rng&)"
description: "Metropolis-Hastings 1D y d-dimensional con proposal Gaussian symmetric. Target log-pdf inyectable via std::function (no necesita normalizarse). Devuelve cadena en out[] y accept rate. Pareja CPU del mc_metropolis_hastings_gpu."
tags: [mcmc, metropolis, hastings, sampling, bayesian, datascience, pendiente-usar]
uses_functions: ["rng_cpp_datascience"]
uses_types: []
returns: []
returns_optional: false
error_type: ""
imports: [cstddef, functional, cmath, vector]
tested: false
tests: []
test_file_path: ""
file_path: "cpp/functions/datascience/metropolis_hastings.cpp"
params:
  - name: target_log_pdf
    desc: "Functor con la log-densidad target (no normalizada). 1D recibe double; ND recibe const double* de tamano d."
  - name: x0
    desc: "Punto inicial (escalar 1D, array d-dim ND)."
  - name: proposal_sigma
    desc: "Stddev del proposal Gaussian. 1D escalar; ND array de d stddevs (uno por dimension)."
  - name: d
    desc: "(ND) dimension del espacio."
  - name: n_samples
    desc: "Total de samples a generar (incluyendo x0)."
  - name: out_chain
    desc: "Buffer destino. 1D: double[n]. ND: double[n*d] row-major (sample i, dim k = out[i*d+k])."
  - name: r
    desc: "Rng (de rng_cpp_datascience). State mutado durante el sampling."
output: "MHResult con n_samples, n_accepted y accept_rate. Tipico target: 0.20-0.40 accept rate (ajustar proposal_sigma)."
---

# metropolis_hastings

Random-walk Metropolis estandar. Replica el sampler que usan los 4 calculadores MCMC del set, en formato componible.

## Patron 1D (mcmc-bayes / mcmc-full Beta posterior)

```cpp
double k = 7.0, n_obs = 10.0;     // observados
auto log_post = [k, n_obs](double theta) -> double {
    if (theta <= 0.0 || theta >= 1.0) return -1e300;
    // Beta(2, 2) prior + Binomial likelihood
    return (k + 1.0) * std::log(theta) +
           (n_obs - k + 1.0) * std::log(1.0 - theta);
};

fn::ds::Rng r;
fn::ds::rng_seed(r, 42);

std::vector<double> chain(10000);
auto res = fn::ds::mh_run_1d(log_post,
                             /*x0=*/0.5,
                             /*sigma=*/0.1,
                             chain.size(),
                             chain.data(),
                             r);
// Burn-in: descartar primeros 1000
// res.accept_rate ~ 0.3 si sigma esta bien ajustada
```

## Patron 2D (mcmc-visualizer)

```cpp
auto log_density = [](const double* xy) -> double {
    double x = xy[0], y = xy[1];
    // Bimodal target del visualizer
    double d1 = (x-2)*(x-2) + (y-2)*(y-2);
    double d2 = (x+2)*(x+2) + (y+2)*(y+2);
    return std::log(std::exp(-0.5*d1) + std::exp(-0.5*d2));
};

double x0[2] = {0.0, 0.0};
double sigma[2] = {1.0, 1.0};
std::vector<double> chain(10000 * 2);

fn::ds::mh_run_nd(log_density, x0, sigma, 2,
                  10000, chain.data(), r);
```

## Notas

- **No hay tuning automatico** del proposal sigma. Si el accept rate es <0.1, baja sigma; si >0.6, sube. Para tunings serios usar adaptive MH (no incluido aqui).
- `target_log_pdf` se evalua 2 veces por sample en el peor caso (current cached, proposal nuevo). Si tu log-pdf es caro (10ms+ por eval), N=10^4 cuesta minutos — para esos casos preferir GPU MH (no caben dependencias caras en GLSL — tienes que portar la log-pdf).
- Compatible con `rhat_split` y `ess_basic`: corre M cadenas con seeds distintos, apila en `chains[m*n + i]`, llama los diagnosticos.
- 1D y ND comparten la maquinaria pero ND lleva el coste de los `std::vector<double>` por sample. Si rendimiento importa y conoces d en compile-time, fork-and-specialize.
