Inference

Package

---

inference

inference

Inference engines for WPPM.

This subpackage provides different strategies for fitting model parameters to data and returning posterior objects.

MVP implementations

• MAPOptimizer : maximum a posteriori fit with Optax optimizers.
• LaplaceApproximation : approximate posterior covariance around MAP.
• LangevinSampler : skeleton for sampling-based inference.

Future extensions

• adjusted MC samplers, e.g., MALA (for Bayesian posterior inference).

Classes:

Name	Description
InferenceEngine	Abstract interface for inference engines.
LangevinSampler	Langevin sampler (stub).
LaplaceApproximation	Laplace approximation around MAP estimate.
MAPOptimizer	MAP (Maximum A Posteriori) optimizer.

Attributes:

Name	Type	Description
INFERENCE_ENGINES

INFERENCE_ENGINES

INFERENCE_ENGINES = {
    "map": MAPOptimizer,
    "laplace": LaplaceApproximation,
    "langevin": LangevinSampler,
}

InferenceEngine

Bases: ABC

Abstract interface for inference engines.

Methods:

Name	Description
fit	Fit model parameters to data and return a Posterior object.

fit

fit(model: Any, data: Any) -> Any

Fit model parameters to data.

Parameters:

Name	Type	Description	Default
model	WPPM	Psychophysical model to fit.	required
data	ResponseData	Observed trials.	required

Returns:

Type	Description
Posterior	Posterior object wrapping fitted params and model reference.

Source code in src/psyphy/inference/base.py

@abstractmethod
def fit(self, model: Any, data: Any) -> Any:
    """
    Fit model parameters to data.

    Parameters
    ----------
    model : WPPM
        Psychophysical model to fit.
    data : ResponseData
        Observed trials.

    Returns
    -------
    Posterior
        Posterior object wrapping fitted params and model reference.
    """
    ...

LangevinSampler

LangevinSampler(
    steps: int = 1000,
    step_size: float = 0.001,
    temperature: float = 1.0,
)

Langevin sampler (stub).

Parameters:

Name	Type	Description	Default
steps	int	Number of Langevin steps.	1000
step_size	float	Integration step size.	1e-3
temperature	float	Noise scale (temperature).	1.0

Methods:

Name	Description
fit	Fit model parameters with Langevin dynamics (stub).

Attributes:

Name	Type	Description
step_size
steps
temperature

Source code in src/psyphy/inference/langevin.py

def __init__(
    self, steps: int = 1000, step_size: float = 1e-3, temperature: float = 1.0
):
    self.steps = steps
    self.step_size = step_size
    self.temperature = temperature

step_size

step_size = step_size

steps

steps = steps

temperature

temperature = temperature

fit

fit(model, data) -> Posterior

Fit model parameters with Langevin dynamics (stub).

Parameters:

Name	Type	Description	Default
model	WPPM	Model instance.	required
data	ResponseData	Observed trials.	required

Returns:

Type	Description
Posterior	Posterior wrapper (MVP: params from init).

Source code in src/psyphy/inference/langevin.py

def fit(self, model, data) -> Posterior:
    """
    Fit model parameters with Langevin dynamics (stub).

    Parameters
    ----------
    model : WPPM
        Model instance.
    data : ResponseData
        Observed trials.

    Returns
    -------
    Posterior
        Posterior wrapper (MVP: params from init).
    """
    return Posterior(params=model.init_params(None), model=model)

LaplaceApproximation

Laplace approximation around MAP estimate.

Methods:

Name	Description
from_map	Construct a Gaussian approximation centered at MAP.

from_map

from_map(map_posterior: Posterior) -> Posterior

Return posterior approximation from MAP.

Parameters:

Name	Type	Description	Default
map_posterior	Posterior	Posterior object from MAP optimization.	required

Returns:

Type	Description
Posterior	Same posterior object (MVP).

Source code in src/psyphy/inference/laplace.py

def from_map(self, map_posterior: Posterior) -> Posterior:
    """
    Return posterior approximation from MAP.

    Parameters
    ----------
    map_posterior : Posterior
        Posterior object from MAP optimization.

    Returns
    -------
    Posterior
        Same posterior object (MVP).
    """
    return map_posterior

MAPOptimizer

MAPOptimizer(
    steps: int = 500,
    learning_rate: float = 5e-05,
    momentum: float = 0.9,
    optimizer: GradientTransformation | None = None,
    *,
    track_history: bool = True,
    log_every: int = 1,
    progress_every: int = 10,
    show_progress: bool = False,
    max_grad_norm: float | None = 1.0,
)

Bases: InferenceEngine

MAP (Maximum A Posteriori) optimizer.

Parameters:

Name	Type	Description	Default
steps	int	Number of optimization steps.	500
optimizer	GradientTransformation	Optax optimizer to use. Default: SGD with momentum.	None

Notes

• Loss function = negative log posterior.
• Gradients computed with jax.grad.

Create a MAP optimizer.

Parameters:

Name	Type	Description	Default
steps	int	Number of optimization steps.	500
optimizer	GradientTransformation | None	Optax optimizer to use.	None
learning_rate	float	Learning rate for the default optimizer (SGD with momentum).	5e-05
momentum	float	Momentum for the default optimizer (SGD with momentum).	0.9
track_history	bool	When True, record loss history during fitting for plotting.	True
log_every	int	Record every N steps (also records the last step).	1
progress_every	int	Update the progress-bar loss display every N steps (and the last step) when show_progress=True. This is kept separate from log_every so you can record loss at high frequency for plotting (e.g. log_every=1) without forcing a device->host sync for the progress UI every step.	10
show_progress	bool	When True, display a tqdm progress bar during fitting. This is a UI feature: if tqdm is not installed, fitting proceeds without a progress bar.	False
max_grad_norm	float | None	If set, clip gradients by global norm to this value before applying optimizer updates. This stabilizes optimization when gradients blow up.	1.0

Methods:

Name	Description
fit	Fit model parameters with MAP optimization.
get_history	Return (steps, losses) recorded during the last fit when tracking was enabled.

Attributes:

Name	Type	Description
log_every
loss_history	list[float]
loss_steps	list[int]
max_grad_norm
optimizer
progress_every
show_progress
steps
track_history

Source code in src/psyphy/inference/map_optimizer.py

def __init__(
    self,
    steps: int = 500,
    learning_rate: float = 5e-5,
    momentum: float = 0.9,
    optimizer: optax.GradientTransformation | None = None,
    *,
    track_history: bool = True,
    log_every: int = 1,
    progress_every: int = 10,
    show_progress: bool = False,
    max_grad_norm: float | None = 1.0,
):
    """Create a MAP optimizer.

    Parameters
    ----------
    steps : int
        Number of optimization steps.
    optimizer : optax.GradientTransformation | None
        Optax optimizer to use.
    learning_rate : float, optional
        Learning rate for the default optimizer (SGD with momentum).
    momentum : float, optional
        Momentum for the default optimizer (SGD with momentum).
    track_history : bool, optional
        When True, record loss history during fitting for plotting.
    log_every : int, optional
        Record every N steps (also records the last step).
    progress_every : int, optional
        Update the progress-bar loss display every N steps (and the last step)
        when show_progress=True.
        This is kept separate from log_every so you can record loss at high
        frequency for plotting (e.g. log_every=1) without forcing a device->host
        sync for the progress UI every step.
    show_progress : bool, optional
        When True, display a tqdm progress bar during fitting.
        This is a UI feature: if tqdm is not installed,
        fitting proceeds without a progress bar.
    max_grad_norm : float | None, optional
        If set, clip gradients by global norm to this value before applying
        optimizer updates. This stabilizes optimization when gradients blow up.
    """
    self.steps = steps
    base_optimizer = optimizer or optax.sgd(
        learning_rate=learning_rate, momentum=momentum
    )
    if max_grad_norm is None:
        self.optimizer = base_optimizer
    else:
        self.optimizer = optax.chain(
            optax.clip_by_global_norm(float(max_grad_norm)),
            base_optimizer,
        )

    self.track_history = track_history
    self.log_every = max(1, int(log_every))
    self.progress_every = max(1, int(progress_every))
    self.show_progress = bool(show_progress)
    self.max_grad_norm = max_grad_norm
    # Exposed after fit() when tracking is enabled
    self.loss_steps: list[int] = []
    self.loss_history: list[float] = []

log_every

log_every = max(1, int(log_every))

loss_history

loss_history: list[float] = []

loss_steps

loss_steps: list[int] = []

max_grad_norm

max_grad_norm = max_grad_norm

optimizer

optimizer = base_optimizer

progress_every

progress_every = max(1, int(progress_every))

show_progress

show_progress = bool(show_progress)

steps

steps = steps

track_history

track_history = track_history

fit

fit(
    model,
    data,
    init_params: dict | None = None,
    seed: int | None = None,
) -> MAPPosterior

Fit model parameters with MAP optimization.

Parameters:

Name	Type	Description	Default
model	WPPM	Model instance.	required
data	ResponseData	Observed trials.	required
init_params	dict | None	Initial parameter PyTree to start optimization from. If provided, this takes precedence over the seed.	None
seed	int | None	PRNG seed used to draw initial parameters from the model's prior when init_params is not provided. If None, defaults to 0.	None

Returns:

Type	Description
MAPPosterior	Posterior wrapper around MAP params and model.

Source code in src/psyphy/inference/map_optimizer.py

def fit(
    self,
    model,
    data,
    init_params: dict | None = None,
    seed: int | None = None,
) -> MAPPosterior:
    """
    Fit model parameters with MAP optimization.

    Parameters
    ----------
    model : WPPM
        Model instance.
    data : ResponseData
        Observed trials.
    init_params : dict | None, optional
        Initial parameter PyTree to start optimization from. If provided,
        this takes precedence over the seed.
    seed : int | None, optional
        PRNG seed used to draw initial parameters from the model's prior
        when init_params is not provided. If None, defaults to 0.

    Returns
    -------
    MAPPosterior
        Posterior wrapper around MAP params and model.
    """

    def loss_fn(params):
        return -model.log_posterior_from_data(params, data)

    # Initialize parameters
    if init_params is not None:
        params = init_params
    else:
        rng_seed = 0 if seed is None else int(seed)
        params = model.init_params(jax.random.PRNGKey(rng_seed))
    opt_state = self.optimizer.init(params)

    @jax.jit
    def step(params, opt_state):
        # Ensure params and opt_state are JAX PyTrees for JIT compatibility
        loss, grads = jax.value_and_grad(loss_fn)(params)  # auto-diff
        updates, opt_state = self.optimizer.update(
            grads, opt_state, params
        )  # optimizer update
        params = optax.apply_updates(params, updates)  # apply updates
        # Only return JAX-compatible types (PyTrees of arrays, scalars)
        return params, opt_state, loss

    # clear any previous history
    if self.track_history:
        self.loss_steps.clear()
        self.loss_history.clear()

    # Optional progress bar.
    #
    # Why we *manually* advance the bar:
    # - When JAX runs on GPU, the first `step(...)` call can spend a long time in
    #   compilation, and tqdm may not visibly advance if the underlying iterator
    #   doesn't get a chance to redraw.
    # - By keeping a normal `range(self.steps)` loop and calling `pbar.update(1)`
    #   ourselves, we ensure the bar advances exactly once per iteration.
    #
    # Performance note: *displaying the loss* requires transferring `loss` from
    # device -> host, which can add sync overhead. We therefore only attach a
    # loss postfix every `progress_every` steps.
    pbar = None
    if self.show_progress:
        try:
            from tqdm.auto import tqdm

            pbar = tqdm(total=self.steps, desc="MAP fit", leave=False)
        except Exception:
            # Soft dependency: tqdm not available (or terminal unsuitable).
            pbar = None

    for i in range(self.steps):
        params, opt_state, loss = step(params, opt_state)

        # Non-finite guard: if loss becomes NaN/Inf, optimization has diverged.
        # Stop early so downstream plots don’t look “truncated” due to NaNs.
        if not bool(jax.numpy.isfinite(loss)):
            if self.track_history:
                try:
                    self.loss_steps.append(i)
                    self.loss_history.append(float(loss))
                except Exception:
                    pass
            print(
                f"[MAPOptimizer] Non-finite loss at step {i}: {loss}. "
                "Stopping early."
            )
            if pbar is not None:
                with contextlib.suppress(Exception):
                    pbar.update(1)
            break

        if self.track_history and (
            (i % self.log_every == 0) or (i == self.steps - 1)
        ):
            # Pull scalar to host and record
            try:
                self.loss_steps.append(i)
                self.loss_history.append(float(loss))
            except Exception:
                #  do not break fitting if logging fails
                pass

        #  progress bar loss display (avoid host sync every step)
        if pbar is not None and (
            (i % self.progress_every == 0) or (i == self.steps - 1)
        ):
            with contextlib.suppress(Exception):
                pbar.set_postfix(loss=float(loss))

        if pbar is not None:
            with contextlib.suppress(Exception):
                pbar.update(1)
                # Encourage a redraw occasionally in environments with buffered/stale
                # TTY updates.
                if (i % self.progress_every == 0) or (i == self.steps - 1):
                    pbar.refresh()

    if pbar is not None:
        with contextlib.suppress(Exception):
            pbar.close()

    return MAPPosterior(params=params, model=model)

get_history

get_history() -> tuple[list[int], list[float]]

Return (steps, losses) recorded during the last fit when tracking was enabled.

Source code in src/psyphy/inference/map_optimizer.py

def get_history(self) -> tuple[list[int], list[float]]:
    """Return (steps, losses) recorded during the last fit when tracking was enabled."""
    return self.loss_steps, self.loss_history

---

Base

---

base

base.py

Abstract base class for inference engines.

All inference engines must implement a fit(model, data) method that returns a Posterior object.

All inference engines (MAPOptimizer, LangevinSampler, LaplaceApproximation) subclass from this base.

Classes:

Name	Description
InferenceEngine	Abstract interface for inference engines.

InferenceEngine

Bases: ABC

Abstract interface for inference engines.

Methods:

Name	Description
fit	Fit model parameters to data and return a Posterior object.

fit

fit(model: Any, data: Any) -> Any

Fit model parameters to data.

Parameters:

Name	Type	Description	Default
model	WPPM	Psychophysical model to fit.	required
data	ResponseData	Observed trials.	required

Returns:

Type	Description
Posterior	Posterior object wrapping fitted params and model reference.

Source code in src/psyphy/inference/base.py

@abstractmethod
def fit(self, model: Any, data: Any) -> Any:
    """
    Fit model parameters to data.

    Parameters
    ----------
    model : WPPM
        Psychophysical model to fit.
    data : ResponseData
        Observed trials.

    Returns
    -------
    Posterior
        Posterior object wrapping fitted params and model reference.
    """
    ...

---

MAP Optimizer

---

map_optimizer

map_optimizer.py

MAP (Maximum A Posteriori) optimizer using Optax.

MVP implementation: - Uses gradient ascent on log posterior. - Defaults to SGD with momentum, but any Optax optimizer can be passed in.

Connections

• Calls WPPM.log_posterior_from_data(params, data) as the objective.
• Returns a Posterior object wrapping the MAP estimate.

Classes:

Name	Description
MAPOptimizer	MAP (Maximum A Posteriori) optimizer.

MAPOptimizer

MAPOptimizer(
    steps: int = 500,
    learning_rate: float = 5e-05,
    momentum: float = 0.9,
    optimizer: GradientTransformation | None = None,
    *,
    track_history: bool = True,
    log_every: int = 1,
    progress_every: int = 10,
    show_progress: bool = False,
    max_grad_norm: float | None = 1.0,
)

Bases: InferenceEngine

MAP (Maximum A Posteriori) optimizer.

Parameters:

Name	Type	Description	Default
steps	int	Number of optimization steps.	500
optimizer	GradientTransformation	Optax optimizer to use. Default: SGD with momentum.	None

Notes

• Loss function = negative log posterior.
• Gradients computed with jax.grad.

Create a MAP optimizer.

Parameters:

Name	Type	Description	Default
steps	int	Number of optimization steps.	500
optimizer	GradientTransformation | None	Optax optimizer to use.	None
learning_rate	float	Learning rate for the default optimizer (SGD with momentum).	5e-05
momentum	float	Momentum for the default optimizer (SGD with momentum).	0.9
track_history	bool	When True, record loss history during fitting for plotting.	True
log_every	int	Record every N steps (also records the last step).	1
progress_every	int	Update the progress-bar loss display every N steps (and the last step) when show_progress=True. This is kept separate from log_every so you can record loss at high frequency for plotting (e.g. log_every=1) without forcing a device->host sync for the progress UI every step.	10
show_progress	bool	When True, display a tqdm progress bar during fitting. This is a UI feature: if tqdm is not installed, fitting proceeds without a progress bar.	False
max_grad_norm	float | None	If set, clip gradients by global norm to this value before applying optimizer updates. This stabilizes optimization when gradients blow up.	1.0

Methods:

Name	Description
fit	Fit model parameters with MAP optimization.
get_history	Return (steps, losses) recorded during the last fit when tracking was enabled.

Attributes:

Name	Type	Description
log_every
loss_history	list[float]
loss_steps	list[int]
max_grad_norm
optimizer
progress_every
show_progress
steps
track_history

Source code in src/psyphy/inference/map_optimizer.py

def __init__(
    self,
    steps: int = 500,
    learning_rate: float = 5e-5,
    momentum: float = 0.9,
    optimizer: optax.GradientTransformation | None = None,
    *,
    track_history: bool = True,
    log_every: int = 1,
    progress_every: int = 10,
    show_progress: bool = False,
    max_grad_norm: float | None = 1.0,
):
    """Create a MAP optimizer.

    Parameters
    ----------
    steps : int
        Number of optimization steps.
    optimizer : optax.GradientTransformation | None
        Optax optimizer to use.
    learning_rate : float, optional
        Learning rate for the default optimizer (SGD with momentum).
    momentum : float, optional
        Momentum for the default optimizer (SGD with momentum).
    track_history : bool, optional
        When True, record loss history during fitting for plotting.
    log_every : int, optional
        Record every N steps (also records the last step).
    progress_every : int, optional
        Update the progress-bar loss display every N steps (and the last step)
        when show_progress=True.
        This is kept separate from log_every so you can record loss at high
        frequency for plotting (e.g. log_every=1) without forcing a device->host
        sync for the progress UI every step.
    show_progress : bool, optional
        When True, display a tqdm progress bar during fitting.
        This is a UI feature: if tqdm is not installed,
        fitting proceeds without a progress bar.
    max_grad_norm : float | None, optional
        If set, clip gradients by global norm to this value before applying
        optimizer updates. This stabilizes optimization when gradients blow up.
    """
    self.steps = steps
    base_optimizer = optimizer or optax.sgd(
        learning_rate=learning_rate, momentum=momentum
    )
    if max_grad_norm is None:
        self.optimizer = base_optimizer
    else:
        self.optimizer = optax.chain(
            optax.clip_by_global_norm(float(max_grad_norm)),
            base_optimizer,
        )

    self.track_history = track_history
    self.log_every = max(1, int(log_every))
    self.progress_every = max(1, int(progress_every))
    self.show_progress = bool(show_progress)
    self.max_grad_norm = max_grad_norm
    # Exposed after fit() when tracking is enabled
    self.loss_steps: list[int] = []
    self.loss_history: list[float] = []

log_every

log_every = max(1, int(log_every))

loss_history

loss_history: list[float] = []

loss_steps

loss_steps: list[int] = []

max_grad_norm

max_grad_norm = max_grad_norm

optimizer

optimizer = base_optimizer

progress_every

progress_every = max(1, int(progress_every))

show_progress

show_progress = bool(show_progress)

steps

steps = steps

track_history

track_history = track_history

fit

fit(
    model,
    data,
    init_params: dict | None = None,
    seed: int | None = None,
) -> MAPPosterior

Fit model parameters with MAP optimization.

Parameters:

Name	Type	Description	Default
model	WPPM	Model instance.	required
data	ResponseData	Observed trials.	required
init_params	dict | None	Initial parameter PyTree to start optimization from. If provided, this takes precedence over the seed.	None
seed	int | None	PRNG seed used to draw initial parameters from the model's prior when init_params is not provided. If None, defaults to 0.	None

Returns:

Type	Description
MAPPosterior	Posterior wrapper around MAP params and model.

Source code in src/psyphy/inference/map_optimizer.py

def fit(
    self,
    model,
    data,
    init_params: dict | None = None,
    seed: int | None = None,
) -> MAPPosterior:
    """
    Fit model parameters with MAP optimization.

    Parameters
    ----------
    model : WPPM
        Model instance.
    data : ResponseData
        Observed trials.
    init_params : dict | None, optional
        Initial parameter PyTree to start optimization from. If provided,
        this takes precedence over the seed.
    seed : int | None, optional
        PRNG seed used to draw initial parameters from the model's prior
        when init_params is not provided. If None, defaults to 0.

    Returns
    -------
    MAPPosterior
        Posterior wrapper around MAP params and model.
    """

    def loss_fn(params):
        return -model.log_posterior_from_data(params, data)

    # Initialize parameters
    if init_params is not None:
        params = init_params
    else:
        rng_seed = 0 if seed is None else int(seed)
        params = model.init_params(jax.random.PRNGKey(rng_seed))
    opt_state = self.optimizer.init(params)

    @jax.jit
    def step(params, opt_state):
        # Ensure params and opt_state are JAX PyTrees for JIT compatibility
        loss, grads = jax.value_and_grad(loss_fn)(params)  # auto-diff
        updates, opt_state = self.optimizer.update(
            grads, opt_state, params
        )  # optimizer update
        params = optax.apply_updates(params, updates)  # apply updates
        # Only return JAX-compatible types (PyTrees of arrays, scalars)
        return params, opt_state, loss

    # clear any previous history
    if self.track_history:
        self.loss_steps.clear()
        self.loss_history.clear()

    # Optional progress bar.
    #
    # Why we *manually* advance the bar:
    # - When JAX runs on GPU, the first `step(...)` call can spend a long time in
    #   compilation, and tqdm may not visibly advance if the underlying iterator
    #   doesn't get a chance to redraw.
    # - By keeping a normal `range(self.steps)` loop and calling `pbar.update(1)`
    #   ourselves, we ensure the bar advances exactly once per iteration.
    #
    # Performance note: *displaying the loss* requires transferring `loss` from
    # device -> host, which can add sync overhead. We therefore only attach a
    # loss postfix every `progress_every` steps.
    pbar = None
    if self.show_progress:
        try:
            from tqdm.auto import tqdm

            pbar = tqdm(total=self.steps, desc="MAP fit", leave=False)
        except Exception:
            # Soft dependency: tqdm not available (or terminal unsuitable).
            pbar = None

    for i in range(self.steps):
        params, opt_state, loss = step(params, opt_state)

        # Non-finite guard: if loss becomes NaN/Inf, optimization has diverged.
        # Stop early so downstream plots don’t look “truncated” due to NaNs.
        if not bool(jax.numpy.isfinite(loss)):
            if self.track_history:
                try:
                    self.loss_steps.append(i)
                    self.loss_history.append(float(loss))
                except Exception:
                    pass
            print(
                f"[MAPOptimizer] Non-finite loss at step {i}: {loss}. "
                "Stopping early."
            )
            if pbar is not None:
                with contextlib.suppress(Exception):
                    pbar.update(1)
            break

        if self.track_history and (
            (i % self.log_every == 0) or (i == self.steps - 1)
        ):
            # Pull scalar to host and record
            try:
                self.loss_steps.append(i)
                self.loss_history.append(float(loss))
            except Exception:
                #  do not break fitting if logging fails
                pass

        #  progress bar loss display (avoid host sync every step)
        if pbar is not None and (
            (i % self.progress_every == 0) or (i == self.steps - 1)
        ):
            with contextlib.suppress(Exception):
                pbar.set_postfix(loss=float(loss))

        if pbar is not None:
            with contextlib.suppress(Exception):
                pbar.update(1)
                # Encourage a redraw occasionally in environments with buffered/stale
                # TTY updates.
                if (i % self.progress_every == 0) or (i == self.steps - 1):
                    pbar.refresh()

    if pbar is not None:
        with contextlib.suppress(Exception):
            pbar.close()

    return MAPPosterior(params=params, model=model)

get_history

get_history() -> tuple[list[int], list[float]]

Return (steps, losses) recorded during the last fit when tracking was enabled.

Source code in src/psyphy/inference/map_optimizer.py

def get_history(self) -> tuple[list[int], list[float]]:
    """Return (steps, losses) recorded during the last fit when tracking was enabled."""
    return self.loss_steps, self.loss_history

---

Langevin Samplers

---

langevin

langevin.py

Langevin samplers for posterior inference.

Implements: - Overdamped (unadjusted) Langevin Algorithm (ULA) - Underdamped Langevin (with BAOAB splitting scheme?)

Used for posterior-aware trial placement (InfoGain).

MVP implementation: - Stub that returns an initial Posterior. - Future: implement underdamped Langevin dynamics (e.g. BAOAB integrator).

Classes:

Name	Description
LangevinSampler	Langevin sampler (stub).

LangevinSampler

LangevinSampler(
    steps: int = 1000,
    step_size: float = 0.001,
    temperature: float = 1.0,
)

Langevin sampler (stub).

Parameters:

Name	Type	Description	Default
steps	int	Number of Langevin steps.	1000
step_size	float	Integration step size.	1e-3
temperature	float	Noise scale (temperature).	1.0

Methods:

Name	Description
fit	Fit model parameters with Langevin dynamics (stub).

Attributes:

Name	Type	Description
step_size
steps
temperature

Source code in src/psyphy/inference/langevin.py

def __init__(
    self, steps: int = 1000, step_size: float = 1e-3, temperature: float = 1.0
):
    self.steps = steps
    self.step_size = step_size
    self.temperature = temperature

step_size

step_size = step_size

steps

steps = steps

temperature

temperature = temperature

fit

fit(model, data) -> Posterior

Fit model parameters with Langevin dynamics (stub).

Parameters:

Name	Type	Description	Default
model	WPPM	Model instance.	required
data	ResponseData	Observed trials.	required

Returns:

Type	Description
Posterior	Posterior wrapper (MVP: params from init).

Source code in src/psyphy/inference/langevin.py

def fit(self, model, data) -> Posterior:
    """
    Fit model parameters with Langevin dynamics (stub).

    Parameters
    ----------
    model : WPPM
        Model instance.
    data : ResponseData
        Observed trials.

    Returns
    -------
    Posterior
        Posterior wrapper (MVP: params from init).
    """
    return Posterior(params=model.init_params(None), model=model)

---

Laplace Approximation

---

laplace

laplace.py

Laplace approximation to posterior.

Approximates posterior with a Gaussian: N(mean = MAP, covariance = H^-1 at MAP)

Provides posterior.sample() cheaply. Useful for InfoGainPlacement when only MAP fit is available.

MVP implementation: - Stub that just returns the MAP posterior. - Future: compute covariance from Hessian at MAP params.

Classes:

Name	Description
LaplaceApproximation	Laplace approximation around MAP estimate.

LaplaceApproximation

Laplace approximation around MAP estimate.

Methods:

Name	Description
from_map	Construct a Gaussian approximation centered at MAP.

from_map

from_map(map_posterior: Posterior) -> Posterior

Return posterior approximation from MAP.

Parameters:

Name	Type	Description	Default
map_posterior	Posterior	Posterior object from MAP optimization.	required

Returns:

Type	Description
Posterior	Same posterior object (MVP).

Source code in src/psyphy/inference/laplace.py

def from_map(self, map_posterior: Posterior) -> Posterior:
    """
    Return posterior approximation from MAP.

    Parameters
    ----------
    map_posterior : Posterior
        Posterior object from MAP optimization.

    Returns
    -------
    Posterior
        Same posterior object (MVP).
    """
    return map_posterior

---